1. Usage

The CUDA Profiling Tools Interface (CUPTI) enables the creation of profiling and tracing tools that target CUDA applications. CUPTI provides four APIs: the Activity API, the Callback API, the Event API, and the Metric API. Using these APIs, you can develop profiling tools that give insight into the CPU and GPU behavior of CUDA applications. CUPTI is delivered as a dynamic library on all platforms supported by CUDA.

1.1. CUPTI Compatibility and Requirements

New versions of the CUDA driver are backwards compatible with older versions of CUPTI. For example, a developer using a profiling tool based on CUPTI 7.0 can update to a more recently released CUDA driver. However, new versions of CUPTI are not backwards compatible with older versions of the CUDA driver. For example, a developer using a profiling tool based on CUPTI 7.0 must have a version of the CUDA driver released with CUDA Toolkit 7.0 (or later) installed as well. CUPTI calls will fail with CUPTI_ERROR_NOT_INITIALIZED if the CUDA driver version is not compatible with the CUPTI version.

1.2. CUPTI Initialization

CUPTI initialization occurs lazily the first time you invoke any CUPTI function. For the Activity, Event, Metric, and Callback APIs there are no requirements on when this initialization must occur (i.e. you can invoke the first CUPTI function at any point). See the CUPTI Activity API section for more information on CUPTI initialization requirements for the activity API.

1.3. CUPTI Activity API

The CUPTI Activity API allows you to asynchronously collect a trace of an application's CPU and GPU CUDA activity. The following terminology is used by the activity API.

Activity Record
CPU and GPU activity is reported in C data structures called activity records. There is a different C structure type for each activity kind (e.g. CUpti_ActivityMemcpy). Records are generically referred to using the CUpti_Activity type. This type contains only a kind field that indicates the kind of the activity record. Using this kind, the object can be cast from the generic CUpti_Activity type to the specific type representing the activity. See the printActivity function in the activity_trace_async sample for an example.
Activity Buffer
An activity buffer is used to transfer one or more activity records from CUPTI to the client. CUPTI fills activity buffers with activity records as the corresponding activities occur on the CPU and GPU. The CUPTI client is responsible for providing empty activity buffers as necessary to ensure that no records are dropped.

An asynchronous buffering API is implemented by cuptiActivityRegisterCallbacks and cuptiActivityFlushAll.

It is not required that the activity API be initalized before CUDA initialization. All related activities occuring after initializing the activity API are collected. You can force initialization of the activity API by enabling one or more activity kinds using cuptiActivityEnable or cuptiActivityEnableContext, as shown in the initTrace function of the activity_trace_async sample. Some activity kinds cannot be directly enabled, see the API documentation for for CUpti_ActivityKind for details. Functions cuptiActivityEnable and cuptiActivityEnableContext will return CUPTI_ERROR_NOT_COMPATIBLE if the requested activity kind cannot be enabled.

The activity buffer API uses callbacks to request and return buffers of activity records. To use the asynchronous buffering API you must first register two callbacks using cuptiActivityRegisterCallbacks. One of these callbacks will be invoked whenever CUPTI needs an empty activity buffer. The other callback is used to deliver a buffer containing one or more activity records to the client. To minimize profiling overhead the client should return as quickly as possible from these callbacks. Function cuptiActivityFlushAll can be used to force CUPTI to deliver any activity buffers that contain completed activity records. Functions cuptiActivityGetAttribute and cuptiActivitySetAttribute can be used to read and write attributes that control how the buffering API behaves. See the API documentation for more information.

The activity_trace_async sample shows how to use the activity buffer API to collect a trace of CPU and GPU activity for a simple application.

1.3.1. SASS Source Correlation

While high-level languages for GPU programming like CUDA C offer a useful level of abstraction, convenience, and maintainability, they inherently hide some of the details of the execution on the hardware. It is sometimes helpful to analyze performance problems for a kernel at the assembly instruction level. Reading assembly language is tedious and challenging; CUPTI can help you to build the correlation between lines in your high-level source code and the executed assembly instructions.
Building SASS source correlation for a PC can be split into two parts -
  • Correlation of the PC to SASS instruction - subscribe to any one of CUPTI_CBID_RESOURCE_MODULE_LOADED or CUPTI_CBID_RESOURCE_MODULE_UNLOAD_STARTING or CUPTI_CBID_RESOURCE_MODULE_PROFILED callbacks. This returns a CUpti_ModuleResourceData structure having the CUDA binary. The binary can be disassembled using nvdisasm utility that comes with the CUDA toolkit. An application can have multiple functions and modules, to uniquely identify there is a functionId field in all source level activity records. This uniquely corresponds to a CUPTI_ACTIVITY_KIND_FUNCTION which has the unique module ID and function ID in the module.
  • Correlation of the SASS instruction to CUDA source line - every source level activity has a sourceLocatorId field which uniquely maps to a record of kind CUPTI_ACTIVITY_KIND_SOURCE_LOCATOR containing the line and file name information. Please note that multiple PCs can correspond to single source line.

When any source level activity (global access, branch, PC Sampling etc) is enabled, source locator record is generated for the PCs that have the source level results. Record CUpti_ActivityInstructionCorrelation can be used along with source level activities to generate SASS assembly instructions to CUDA C source code mapping for all the PCs of the function and not just the PCs that have the source level results. This can be enabled using activity kind CUPTI_ACTIVITY_KIND_INSTRUCTION_CORRELATION.

The sass_source_map sample shows how to map SASS assembly instructions to CUDA C source.

1.3.2. PC Sampling

CUPTI supports device-wide sampling of the program counter (PC). The PC Sampling gives the number of samples for each source and assembly line with various stall reasons. Using this information you can pinpoint portions of your kernel that are introducing latencies and the reason for the latency. Samples are taken in round robin order for all active warps at a fixed number of cycles regardless of whether the warp is issuing an instruction or not.
Activity record CUpti_ActivityPCSampling enabled using activity kind CUPTI_ACTIVITY_KIND_PC_SAMPLING outputs stall reason along with PC and other related information. Enum CUpti_ActivityPCSamplingStallReason lists all the stall reasons. Sampling period is configurable and can be tuned using API cuptiActivityConfigurePCSampling. Activity record CUpti_ActivityPCSamplingRecordInfo provides the total and dropped samples for each kernel profiled for PC sampling. This feature is available on devices with compute capability 5.2.

The pc_sampling sample shows how to use the these APIs to collect PC Sampling profiling information for a kernel.

1.4. CUPTI Callback API

The CUPTI Callback API allows you to register a callback into your own code. Your callback will be invoked when the application being profiled calls a CUDA runtime or driver function, or when certain events occur in the CUDA driver. The following terminology is used by the callback API.

Callback Domain
Callbacks are grouped into domains to make it easier to associate your callback functions with groups of related CUDA functions or events. There are currently four callback domains, as defined by CUpti_CallbackDomain: a domain for CUDA runtime functions, a domain for CUDA driver functions, a domain for CUDA resource tracking, and a domain for CUDA synchronization notification.
Callback ID
Each callback is given a unique ID within the corresponding callback domain so that you can identify it within your callback function. The CUDA driver API IDs are defined in cupti_driver_cbid.h and the CUDA runtime API IDs are defined in cupti_runtime_cbid.h. Both of these headers are included for you when you include cupti.h. The CUDA resource callback IDs are defined by CUpti_CallbackIdResource and the CUDA synchronization callback IDs are defined by CUpti_CallbackIdSync.
Callback Function
Your callback function must be of type CUpti_CallbackFunc. This function type has two arguments that specify the callback domain and ID so that you know why the callback is occurring. The type also has a cbdata argument that is used to pass data specific to the callback.
Subscriber
A subscriber is used to associate each of your callback functions with one or more CUDA API functions. There can be at most one subscriber initialized with cuptiSubscribe() at any time. Before initializing a new subscriber, the existing subscriber must be finalized with cuptiUnsubscribe().

Each callback domain is described in detail below. Unless explicitly stated, it is not supported to call any CUDA runtime or driver API from within a callback function. Doing so may cause the application to hang.

1.4.1. Driver and Runtime API Callbacks

Using the callback API with the CUPTI_CB_DOMAIN_DRIVER_API or CUPTI_CB_DOMAIN_RUNTIME_API domains, you can associate a callback function with one or more CUDA API functions. When those CUDA functions are invoked in the application, your callback function is invoked as well. For these domains, the cbdata argument to your callback function will be of the type CUpti_CallbackData.

It is legal to call cudaThreadSynchronize(), cudaDeviceSynchronize(), cudaStreamSynchronize(), cuCtxSynchronize(), and cuStreamSynchronize() from within a driver or runtime API callback function.

The following code shows a typical sequence used to associate a callback function with one or more CUDA API functions. To simplify the presentation error checking code has been removed. 

  CUpti_SubscriberHandle subscriber;
  MyDataStruct *my_data = ...;
  ...
  cuptiSubscribe(&subscriber, 
                 (CUpti_CallbackFunc)my_callback , my_data);
  cuptiEnableDomain(1, subscriber, 
                    CUPTI_CB_DOMAIN_RUNTIME_API);

First, cuptiSubscribe is used to initialize a subscriber with the my_callback callback function. Next, cuptiEnableDomain is used to associate that callback with all the CUDA runtime API functions. Using this code sequence will cause my_callback to be called twice each time any of the CUDA runtime API functions are invoked, once on entry to the CUDA function and once just before exit from the CUDA function. CUPTI callback API functions cuptiEnableCallback and cuptiEnableAllDomains can also be used to associate CUDA API functions with a callback (see reference below for more information).

The following code shows a typical callback function.

void CUPTIAPI
my_callback(void *userdata, CUpti_CallbackDomain domain,
            CUpti_CallbackId cbid, const void *cbdata)
{
  const CUpti_CallbackData *cbInfo = (CUpti_CallbackData *)cbdata;
  MyDataStruct *my_data = (MyDataStruct *)userdata;
      
  if ((domain == CUPTI_CB_DOMAIN_RUNTIME_API) &&
      (cbid == CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy_v3020))  { 
    if (cbInfo->callbackSite == CUPTI_API_ENTER) {
        cudaMemcpy_v3020_params *funcParams = 
             (cudaMemcpy_v3020_params *)(cbInfo->
                 functionParams);

        size_t count = funcParams->count;
        enum cudaMemcpyKind kind = funcParams->kind;
        ...
      }
  ...

In your callback function, you use the CUpti_CallbackDomain and CUpti_CallbackID parameters to determine which CUDA API function invocation is causing this callback. In the example above, we are checking for the CUDA runtime cudaMemcpy function. The cbdata parameter holds a structure of useful information that can be used within the callback. In this case we use the callbackSite member of the structure to detect that the callback is occurring on entry to cudaMemcpy, and we use the functionParams member to access the parameters that were passed to cudaMemcpy. To access the parameters we first cast functionParams to a structure type corresponding to the cudaMemcpy function. These parameter structures are contained in generated_cuda_runtime_api_meta.h, generated_cuda_meta.h, and a number of other files. When possible these files are included for you by cupti.h.

The callback_event and callback_timestamp samples described on the samples page both show how to use the callback API for the driver and runtime API domains.

1.4.2. Resource Callbacks

Using the callback API with the CUPTI_CB_DOMAIN_RESOURCE domain, you can associate a callback function with some CUDA resource creation and destruction events. For example, when a CUDA context is created, your callback function will be invoked with a callback ID equal to CUPTI_CBID_RESOURCE_CONTEXT_CREATED. For this domain, the cbdata argument to your callback function will be of the type CUpti_ResourceData.

Note that, APIs cuptiActivityFlush and cuptiActivityFlushAll will result in deadlock when called from stream destroy starting callback identified using callback ID CUPTI_CBID_RESOURCE_STREAM_DESTROY_STARTING.

1.4.3. Synchronization Callbacks

Using the callback API with the CUPTI_CB_DOMAIN_SYNCHRONIZE domain, you can associate a callback function with CUDA context and stream synchronizations. For example, when a CUDA context is synchronized, your callback function will be invoked with a callback ID equal to CUPTI_CBID_SYNCHRONIZE_CONTEXT_SYNCHRONIZED. For this domain, the cbdata argument to your callback function will be of the type CUpti_SynchronizeData.

1.4.4. NVIDIA Tools Extension Callbacks

Using the callback API with the CUPTI_CB_DOMAIN_NVTX domain, you can associate a callback function with NVIDIA Tools Extension (NVTX) API functions. When an NVTX function is invoked in the application, your callback function is invoked as well. For these domains, the cbdata argument to your callback function will be of the type CUpti_NvtxData.

The NVTX library has its own convention for discovering the profiling library that will provide the implementation of the NVTX callbacks. To receive callbacks you must set the NVTX environment variables appropriately so that when the application calls an NVTX function, your profiling library recieve the callbacks. The following code sequence shows a typical initialization sequence to enable NVTX callbacks and activity records. 
/* Set env so CUPTI-based profiling library loads on first nvtx call. */
char *inj32_path = "/path/to/32-bit/version/of/cupti/based/profiling/library";
char *inj64_path = "/path/to/64-bit/version/of/cupti/based/profiling/library";
setenv("NVTX_INJECTION32_PATH", inj32_path, 1);
setenv("NVTX_INJECTION64_PATH", inj64_path, 1);

The following code shows a typical sequence used to associate a callback function with one or more NVTX functions. To simplify the presentation error checking code has been removed. 

CUpti_SubscriberHandle subscriber;
MyDataStruct *my_data = ...;
...
cuptiSubscribe(&subscriber, 
               (CUpti_CallbackFunc)my_callback , my_data);
cuptiEnableDomain(1, subscriber, 
                  CUPTI_CB_DOMAIN_NVTX);

First, cuptiSubscribe is used to initialize a subscriber with the my_callback callback function. Next, cuptiEnableDomain is used to associate that callback with all the NVTX functions. Using this code sequence will cause my_callback to be called once each time any of the NVTX functions are invoked. CUPTI callback API functions cuptiEnableCallback and cuptiEnableAllDomains can also be used to associate NVTX API functions with a callback (see reference below for more information).

The following code shows a typical callback function.

void CUPTIAPI
my_callback(void *userdata, CUpti_CallbackDomain domain,
            CUpti_CallbackId cbid, const void *cbdata)
{
  const CUpti_NvtxData *nvtxInfo = (CUpti_NvtxData *)cbdata;
  MyDataStruct *my_data = (MyDataStruct *)userdata;
      
  if ((domain == CUPTI_CB_DOMAIN_NVTX) &&
      (cbid == NVTX_CBID_CORE_NameOsThreadA))  { 
    nvtxNameOsThreadA_params *params = (nvtxNameOsThreadA_params *)nvtxInfo->
             functionParams;
    ...
  }
  ...

In your callback function, you use the CUpti_CallbackDomain and CUpti_CallbackID parameters to determine which NVTX API function invocation is causing this callback. In the example above, we are checking for the nvtxNameOsThreadA function. The cbdata parameter holds a structure of useful information that can be used within the callback. In this case, we use the functionParams member to access the parameters that were passed to nvtxNameOsThreadA. To access the parameters we first cast functionParams to a structure type corresponding to the nvtxNameOsThreadA function. These parameter structures are contained in generated_nvtx_meta.h.

1.5. CUPTI Event API

The CUPTI Event API allows you to query, configure, start, stop, and read the event counters on a CUDA-enabled device. The following terminology is used by the event API.

Event
An event is a countable activity, action, or occurrence on a device.
Event ID
Each event is assigned a unique identifier. A named event will represent the same activity, action, or occurrence on all device types. But the named event may have different IDs on different device families. Use cuptiEventGetIdFromName to get the ID for a named event on a particular device.
Event Category
Each event is placed in one of the categories defined by CUpti_EventCategory. The category indicates the general type of activity, action, or occurrence measured by the event.
Event Domain
A device exposes one or more event domains. Each event domain represents a group of related events available on that device. A device may have multiple instances of a domain, indicating that the device can simultaneously record multiple instances of each event within that domain.
Event Group
An event group is a collection of events that are managed together. The number and type of events that can be added to an event group are subject to device-specific limits. At any given time, a device may be configured to count events from a limited number of event groups. All events in an event group must belong to the same event domain.
Event Group Set
An event group set is a collection of event groups that can be enabled at the same time. Event group sets are created by cuptiEventGroupSetsCreate and cuptiMetricCreateEventGroupSets.

You can determine the events available on a device using the cuptiDeviceEnumEventDomains and cuptiEventDomainEnumEvents functions. The cupti_query sample described on the samples page shows how to use these functions. You can also enumerate all the CUPTI events available on any device using the cuptiEnumEventDomains function.

Configuring and reading event counts requires the following steps. First, select your event collection mode. If you want to count events that occur during the execution of a kernel, use cuptiSetEventCollectionMode to set mode CUPTI_EVENT_COLLECTION_MODE_KERNEL. If you want to continuously sample the event counts, use mode CUPTI_EVENT_COLLECTION_MODE_CONTINUOUS. Next determine the names of the events that you want to count, and then use the cuptiEventGroupCreate, cuptiEventGetIdFromName, and cuptiEventGroupAddEvent functions to create and initialize an event group with those events. If you are unable to add all the events to a single event group then you will need to create multiple event groups. Alternatively, you can use the cuptiEventGroupSetsCreate function to automatically create the event group(s) required for a set of events.

To begin counting a set of events, enable the event group or groups that contain those events by using the cuptiEventGroupEnable function. If your events are contained in multiple event groups you may be unable to enable all of the event groups at the same time, due to device limitations. In this case, you can gather the events across multiple executions of the application or you can enable kernel replay. If you enable kernel replay using cuptiEnableKernelReplayMode you will be able to enabled any number of event groups and all the contained events will be collect.

Use the cuptiEventGroupReadEvent and/or cuptiEventGroupReadAllEvents functions to read the event values. When you are done collecting events, use the cuptiEventGroupDisable function to stop counting of the events contained in an event group. The callback_event sample described on the samples page shows how to use these functions to create, enable, and disable event groups, and how to read event counts.

In a system with multiple GPUs, events can be collected simultaneously on all the GPUs i.e. event profiling doesn't enforce any serialization of work across GPUs. The event_multi_gpu sample shows how to use the CUPTI event and CUDA APIs on such setups.

1.5.1. Collecting Kernel Execution Events

A common use of the event API is to count a set of events during the execution of a kernel (as demonstrated by the callback_event sample). The following code shows a typical callback used for this purpose. Assume that the callback was enabled only for a kernel launch using the CUDA runtime (i.e. by cuptiEnableCallback(1, subscriber, CUPTI_CB_DOMAIN_RUNTIME_API, CUPTI_RUNTIME_TRACE_CBID_cudaLaunch_v3020). To simplify the presentation error checking code has been removed. 

static void CUPTIAPI
getEventValueCallback(void *userdata,
                      CUpti_CallbackDomain domain,
                      CUpti_CallbackId cbid,
                      const void *cbdata)
{
  const CUpti_CallbackData *cbData = 
                (CUpti_CallbackData *)cbdata;
     
  if (cbData->callbackSite == CUPTI_API_ENTER) {
    cudaDeviceSynchronize();
    cuptiSetEventCollectionMode(cbInfo->context, 
                                CUPTI_EVENT_COLLECTION_MODE_KERNEL);
    cuptiEventGroupEnable(eventGroup);
  }
    
  if (cbData->callbackSite == CUPTI_API_EXIT) {
    cudaDeviceSynchronize();
    cuptiEventGroupReadEvent(eventGroup, 
                             CUPTI_EVENT_READ_FLAG_NONE, 
                             eventId, 
                             &bytesRead, &eventVal);
      
    cuptiEventGroupDisable(eventGroup);
  }
}

Two synchronization points are used to ensure that events are counted only for the execution of the kernel. If the application contains other threads that launch kernels, then additional thread-level synchronization must also be introduced to ensure that those threads do not launch kernels while the callback is collecting events. When the cudaLaunch API is entered (that is, before the kernel is actually launched on the device), cudaDeviceSynchronize is used to wait until the GPU is idle. The event collection mode is set to CUPTI_EVENT_COLLECTION_MODE_KERNEL so that the event counters are automatically started and stopped just before and after the kernel executes. Then event collection is enabled with cuptiEventGroupEnable.

When the cudaLaunch API is exited (that is, after the kernel is queued for execution on the GPU) another cudaDeviceSynchronize is used to cause the CPU thread to wait for the kernel to finish execution. Finally, the event counts are read with cuptiEventGroupReadEvent.

1.5.2. Sampling Events

The event API can also be used to sample event values while a kernel or kernels are executing (as demonstrated by the event_sampling sample). The sample shows one possible way to perform the sampling. The event collection mode is set to CUPTI_EVENT_COLLECTION_MODE_CONTINUOUS so that the event counters run continuously. Two threads are used in event_sampling: one thread schedules the kernels and memcpys that perform the computation, while another thread wakes up periodically to sample an event counter. In this sample there is no correlation of the event samples with what is happening on the GPU. To get some coarse correlation, you can use cuptiDeviceGetTimestamp to collect the GPU timestamp at the time of the sample and also at other interesting points in your application.

1.6. CUPTI Metric API

The CUPTI Metric API allows you to collect application metrics calculated from one or more event values. The following terminology is used by the metric API.

Metric
An characteristic of an application that is calculated from one or more event values.
Metric ID
Each metric is assigned a unique identifier. A named metric will represent the same characteristic on all device types. But the named metric may have different IDs on different device families. Use cuptiMetricGetIdFromName to get the ID for a named metric on a particular device.
Metric Category
Each metric is placed in one of the categories defined by CUpti_MetricCategory. The category indicates the general type of the characteristic measured by the metric.
Metric Property
Each metric is calculated from input values. These input values can be events or properties of the device or system. The available properties are defined by CUpti_MetricPropertyID.
Metric Value
Each metric has a value that represents one of the kinds defined by CUpti_MetricValueKind. For each value kind, there is a corresponding member of the CUpti_MetricValue union that is used to hold the metric's value.

The tables included in this section list the metrics available for each device, as determined by the device's compute capability. You can also determine the metrics available on a device using the cuptiDeviceEnumMetrics function. The cupti_query sample described on the samples page shows how to use this function. You can also enumerate all the CUPTI metrics available on any device using the cuptiEnumMetrics function.

CUPTI provides two functions for calculating a metric value. cuptiMetricGetValue2 can be used to calculate a metric value when the device is not available. All required event values and metric properties must be provided by the caller. cuptiMetricGetValue can be used to calculate a metric value when the device is available (as a CUdevice object). All required event values must be provided by the caller but CUPTI will determine the appropriate property values from the CUdevice object.

Configuring and calculating metric values requires the following steps. First, determine the name of the metric that you want to collect, and then use the cuptiMetricGetIdFromName to get the metric ID. Use cuptiMetricEnumEvents to get the events required to calculate the metric and follow instructions in the CUPTI Event API section to create the event groups for those events. When creating event groups in this manner it is important to use the result of cuptiMetricGetRequiredEventGroupSets to properly group together events that must be collected in the same pass to ensure proper metric calculation.

Alternatively, you can use the cuptiMetricCreateEventGroupSets function to automatically create the event group(s) required for metric's events. When using this function events will be grouped as required to most accurately calculate the metric, as a result it is not necessary to use cuptiMetricGetRequiredEventGroupSets.

If you are using cuptiMetricGetValue2 then you must also collect the required metric property values using cuptiMetricEnumProperties.

Collect event counts as described in the CUPTI Event API section, and then use either cuptiMetricGetValue or cuptiMetricGetValue2 to calculate the metric value from the collected event and property values. The callback_metric sample described on the samples page shows how to use the functions to calculate event values and calculate a metric using cuptiMetricGetValue. Note that, as shown in the example, you should collect event counts from all domain instances and normalize the counts to get the most accurate metric values. It is necessary to normalize the event counts because the number of event counter instances varies by device and by the event being counted.

For example, a device might have 8 multiprocessors but only have event counters for 4 of the multiprocessors, and might have 3 memory units and only have events counters for one memory unit. When calculating a metric that requires a multiprocessor event and a memory unit event, the 4 multiprocessor counters should be summed and multiplied by 2 to normalize the event count across the entire device. Similarly, the one memory unit counter should be multiplied by 3 to normalize the event count across the entire device. The normalized values can then be passed to cuptiMetricGetValue or cuptiMetricGetValue2 to calculate the metric value.

As described, the normalization assumes the kernel executes a sufficient number of blocks to completely load the device. If the kernel has only a small number of blocks, normalizing across the entire device may skew the result.

Metric Reference - Compute Capability 2.x

Devices with compute capability between 2.0, inclusive, and 3.0 implement the metrics shown in the following table. A scope value of single-context indicates that the metric can only be accurately collected when a single context (CUDA or graphics) is executing on the GPU. A scope value of multi-context indicates that the metric can be accurately collected when multiple contexts are executing on the GPU.

Table 1. Capability 2.x Metrics
Metric Name Description Scope
achieved_occupancy Ratio of the average active warps per active cycle to the maximum number of warps supported on a multiprocessor Multi-context
alu_fu_utilization The utilization level of the multiprocessor function units that execute integer and floating-point arithmetic instructions on a scale of 0 to 10 Multi-context
atomic_replay_overhead Average number of replays due to atomic and reduction bank conflicts for each instruction executed Multi-context
atomic_throughput Global memory atomic and reduction throughput Multi-context
atomic_transactions Global memory atomic and reduction transactions Multi-context
atomic_transactions_per_request Average number of global memory atomic and reduction transactions performed for each atomic and reduction instruction Multi-context
branch_efficiency Ratio of non-divergent branches to total branches expressed as percentage Multi-context
cf_executed Number of executed control-flow instructions Multi-context
cf_fu_utilization The utilization level of the multiprocessor function units that execute control-flow instructions on a scale of 0 to 10 Multi-context
cf_issued Number of issued control-flow instructions Multi-context
dram_read_throughput Device memory read throughput Single-context
dram_read_transactions Device memory read transactions Single-context
dram_utilization The utilization level of the device memory relative to the peak utilization on a scale of 0 to 10 Single-context
dram_write_throughput Device memory write throughput Single-context
dram_write_transactions Device memory write transactions Single-context
ecc_throughput ECC throughput from L2 to DRAM Single-context
ecc_transactions Number of ECC transactions between L2 and DRAM Single-context
eligible_warps_per_cycle Average number of warps that are eligible to issue per active cycle Multi-context
flop_count_dp Number of double-precision floating-point operations executed by non-predicated threads (add, multiply, multiply-accumulate and special). Each multiply-accumulate operation contributes 2 to the count. Multi-context
flop_count_dp_add Number of double-precision floating-point add operations executed by non-predicated threads Multi-context
flop_count_dp_fma Number of double-precision floating-point multiply-accumulate operations executed by non-predicated threads. Each multiply-accumulate operation contributes 1 to the count. Multi-context
flop_count_dp_mul Number of double-precision floating-point multiply operations executed by non-predicated threads Multi-context
flop_count_sp Number of single-precision floating-point operations executed by non-predicated threads (add, multiply, multiply-accumulate and special). Each multiply-accumulate operation contributes 2 to the count. Multi-context
flop_count_sp_add Number of single-precision floating-point add operations executed by non-predicated threads Multi-context
flop_count_sp_fma Number of single-precision floating-point multiply-accumulate operations executed by non-predicated threads. Each multiply-accumulate operation contributes 1 to the count. Multi-context
flop_count_sp_mul Number of single-precision floating-point multiply operations executed by non-predicated threads Multi-context
flop_count_sp_special Number of single-precision floating-point special operations executed by non-predicated threads Multi-context
flop_dp_efficiency Ratio of achieved to peak double-precision floating-point operations Multi-context
flop_sp_efficiency Ratio of achieved to peak single-precision floating-point operations Multi-context
gld_efficiency Ratio of requested global memory load throughput to required global memory load throughput expressed as percentage. Values greater than 100% indicate that, on average, the load requests of multiple threads in a warp fetched from the same memory address. If the code has surface loads then the metric will report lower values than actual efficiency. Refer limitation-1* listed below the table. Single-context
gld_requested_throughput Requested global memory load throughput Multi-context
gld_throughput Global memory load throughput. Refer limitation-1* listed below the table. Single-context
gld_transactions Number of global memory load transactions. Refer limitation-1* listed below the table. Single-context
gld_transactions_per_request Average number of surface and global memory load transactions performed for each surface and global memory load. Refer limitation-1* listed below the table. Single-context
global_cache_replay_overhead Average number of replays due to global memory cache misses for each instruction executed. Refer limitation-1* listed below the table. Single-context
gst_efficiency Ratio of requested global memory store throughput to required global memory store throughput expressed as percentage. Values greater than 100% indicate that, on average, the store requests of multiple threads in a warp targeted the same memory address. Single-context
gst_requested_throughput Requested global memory store throughput Multi-context
gst_throughput Global memory store throughput Single-context
gst_transactions Number of global memory store transactions. Refer limitation-1* listed below the table. Single-context
gst_transactions_per_request Average number of surface and global memory store transactions performed for each surface and global memory store. Single-context
inst_bit_convert Number of bit-conversion instructions executed by non-predicated threads Multi-context
inst_compute_ld_st Number of compute load/store instructions executed by non-predicated threads Multi-context
inst_control Number of control-flow instructions executed by non-predicated threads (jump, branch, etc.) Multi-context
inst_executed The number of instructions executed Multi-context
inst_fp_32 Number of single-precision floating-point instructions executed by non-predicated threads (arithmetric, compare, etc.) Multi-context
inst_fp_64 Number of double-precision floating-point instructions executed by non-predicated threads (arithmetric, compare, etc.) Multi-context
inst_integer Number of integer instructions executed by non-predicated threads Multi-context
inst_inter_thread_communication Number of inter-thread communication instructions executed by non-predicated threads Multi-context
inst_issued The number of instructions issued Multi-context
inst_misc Number of miscellaneous instructions executed by non-predicated threads Multi-context
inst_per_warp Average number of instructions executed by each warp Multi-context
inst_replay_overhead Average number of replays for each instruction executed Multi-context
ipc Instructions executed per cycle Multi-context
ipc_instance Instructions executed per cycle for a single multiprocessor Multi-context
issue_slot_utilization Percentage of issue slots that issued at least one instruction, averaged across all cycles Multi-context
issue_slots The number of issue slots used Multi-context
issued_ipc Instructions issued per cycle Multi-context
l1_cache_global_hit_rate Hit rate in L1 cache for global loads. Refer limitation-1* listed below the table. Single-context
l1_cache_local_hit_rate Hit rate in L1 cache for local loads and stores. Refer limitation-1* listed below the table. Single-context
l1_shared_utilization The utilization level of the L1/shared memory relative to peak utilization on a scale of 0 to 10. Refer limitation-1* listed below the table. Single-context
l2_atomic_throughput Memory read throughput seen at L2 cache for atomic and reduction requests Sinlge-context
l2_atomic_transactions Memory read transactions seen at L2 cache for atomic and reduction requests Single-context
l2_l1_read_hit_rate Hit rate at L2 cache for all read requests from L1 cache Sinlge-context
l2_l1_read_throughput Memory read throughput seen at L2 cache for read requests from L1 cache Single-context
l2_l1_read_transactions Memory read transactions seen at L2 cache for all read requests from L1 cache Single-context
l2_l1_write_throughput Memory write throughput seen at L2 cache for write requests from L1 cache Single-context
l2_l1_write_transactions Memory write transactions seen at L2 cache for all write requests from L1 cache Single-context
l2_read_throughput Memory read throughput seen at L2 cache for all read requests Single-context
l2_read_transactions Memory read transactions seen at L2 cache for all read requests Single-context
l2_tex_read_transactions Memory read transactions seen at L2 cache for read requests from the texture cache Single-context
l2_texture_read_hit_rate Hit rate at L2 cache for all read requests from texture cache Single-context
l2_texure_read_throughput Memory read throughput seen at L2 cache for read requests from the texture cache Sinlge-context
l2_utilization The utilization level of the L2 cache relative to the peak utilization on a scale of 0 to 10 Single-context
l2_write_throughput Memory write throughput seen at L2 cache for all write requests Single-context
l2_write_transactions Memory write transactions seen at L2 cache for all write requests Single-context
ldst_executed Number of executed load and store instructions Multi-context
ldst_fu_utilization The utilization level of the multiprocessor function units that execute global, local and shared memory instructions on a scale of 0 to 10 Multi-context
ldst_issued Number of issued load and store instructions Multi-context
local_load_throughput Local memory load throughput. Refer limitation-1* listed below the table. Single-context
local_load_transactions Number of local memory load transactions. Refer limitation-1* listed below the table. Single-context
local_load_transactions_per_request Average number of local memory load transactions performed for each local memory load. Refer limitation-1* listed below the table. Single-context
local_memory_overhead Ratio of local memory traffic to total memory traffic between the L1 and L2 caches expressed as percentage. Refer limitation-1* listed below the table. Single-context
local_replay_overhead Average number of replays due to local memory accesses for each instruction executed. Refer limitation-1* listed below the table. Single-context
local_store_throughput Local memory store throughput. Refer limitation-1* listed below the table. Single-context
local_store_transactions Number of local memory store transactions. Refer limitation-1* listed below the table. Single-context
local_store_transactions_per_request Average number of local memory store transactions performed for each local memory store. Refer limitation-1* listed below the table. Single-context
shared_efficiency Ratio of requested shared memory throughput to required shared memory throughput expressed as percentage. Refer limitation-1* listed below the table. Single-context
shared_load_throughput Shared memory load throughput. Refer limitation-1* listed below the table. Single-context
shared_load_transactions Number of shared memory load transactions. Refer limitation-1* listed below the table. Single-context
shared_load_transactions_per_request Average number of shared memory load transactions performed for each shared memory load. Refer limitation-1* listed below the table. Single-context
shared_replay_overhead Average number of replays due to shared memory conflicts for each instruction executed. Refer limitation-1* listed below the table. Single-context
shared_store_throughput Shared memory store throughput. Refer limitation-1* listed below the table. Single-context
shared_store_transactions Number of shared memory store transactions. Refer limitation-1* listed below the table. Single-context
shared_store_transactions_per_request Average number of shared memory store transactions performed for each shared memory store. Refer limitation-1* listed below the table. Single-context
sm_efficiency The percentage of time at least one warp is active on a multiprocessor averaged over all multiprocessors on the GPU Single-context
sm_efficiency_instance The percentage of time at least one warp is active on a specific multiprocessor Single-context
stall_data_request Percentage of stalls occurring because a memory operation cannot be performed due to the required resources not being available or fully utilized, or because too many requests of a given type are outstanding Multi-context
stall_exec_dependency Percentage of stalls occurring because an input required by the instruction is not yet available Multi-context
stall_inst_fetch Percentage of stalls occurring because the next assembly instruction has not yet been fetched Multi-context
stall_other Percentage of stalls occurring due to miscellaneous reasons Multi-context
stall_sync Percentage of stalls occurring because the warp is blocked at a __syncthreads() call Multi-context
stall_texture Percentage of stalls occurring because the texture sub-system is fully utilized or has too many outstanding requests Multi-context
sysmem_read_throughput System memory read throughput Single-context
sysmem_read_transactions System memory read transactions Single-context
sysmem_utilization The utilization level of the system memory relative to the peak utilization on a scale of 0 to 10 Single-context
sysmem_write_throughput System memory write throughput Single-context
sysmem_write_transactions System memory write transactions Single-context
tex_cache_hit_rate Texture cache hit rate. Refer limitation-1* listed below the table. Single-context
tex_cache_throughput Texture cache throughput. Refer limitation-1* listed below the table. Single-context
tex_cache_transactions Texture cache read transactions. Refer limitation-1* listed below the table. Single-context
tex_fu_utilization The utilization level of the multiprocessor function units that execute texture instructions on a scale of 0 to 10 Multi-context
tex_utilization The utilization level of the texture cache relative to the peak utilization on a scale of 0 to 10. Refer limitation-1* listed below the table. Single-context
warp_execution_efficiency Ratio of the average active threads per warp to the maximum number of threads per warp supported on a multiprocessor expressed as percentage Multi-context

* Limitation-1: The metric value may not be accurate as some of the events used are collected only for few multiprocessor instances and are extrapolated to cover total number of multiprocessors available in the GPU.

Metric Reference - Compute Capability 3.x

Devices with compute capability between 3.0, inclusive, and 4.0 implement the metrics shown in the following table. Starting CUDA Toolkit 7.0 all metrics can be collected accurately when multiple contexts (CUDA and/or graphics) are executing on the GPU. Note that for some metrics the multi-context scope is supported only for specific devices. Such metrics are marked with "Multi-context*" under the "Scope" column. Refer the note at the bottom of the table.

Table 2. Capability 3.x Metrics
Metric Name Description Scope
achieved_occupancy Ratio of the average active warps per active cycle to the maximum number of warps supported on a multiprocessor Multi-context
alu_fu_utilization The utilization level of the multiprocessor function units that execute integer and floating-point arithmetic instructions on a scale of 0 to 10 Multi-context
atomic_replay_overhead Average number of replays due to atomic and reduction bank conflicts for each instruction executed Multi-context
atomic_throughput Global memory atomic and reduction throughput Multi-context
atomic_transactions Global memory atomic and reduction transactions Multi-context
atomic_transactions_per_request Average number of global memory atomic and reduction transactions performed for each atomic and reduction instruction Multi-context
branch_efficiency Ratio of non-divergent branches to total branches expressed as percentage. This is available for compute capability 3.0. Multi-context
cf_executed Number of executed control-flow instructions Multi-context
cf_fu_utilization The utilization level of the multiprocessor function units that execute control-flow instructions on a scale of 0 to 10 Multi-context
cf_issued Number of issued control-flow instructions Multi-context
dram_read_throughput Device memory read throughput. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
dram_read_transactions Device memory read transactions. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
dram_utilization The utilization level of the device memory relative to the peak utilization on a scale of 0 to 10 Multi-context*
dram_write_throughput Device memory write throughput. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
dram_write_transactions Device memory write transactions. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
ecc_throughput ECC throughput from L2 to DRAM. This is available for compute capability 3.5 and 3.7. Multi-context*
ecc_transactions Number of ECC transactions between L2 and DRAM. This is available for compute capability 3.5 and 3.7. Multi-context*
eligible_warps_per_cycle Average number of warps that are eligible to issue per active cycle Multi-context
flop_count_dp Number of double-precision floating-point operations executed by non-predicated threads (add, multiply, multiply-accumulate and special). Each multiply-accumulate operation contributes 2 to the count. Multi-context
flop_count_dp_add Number of double-precision floating-point add operations executed by non-predicated threads Multi-context
flop_count_dp_fma Number of double-precision floating-point multiply-accumulate operations executed by non-predicated threads. Each multiply-accumulate operation contributes 1 to the count. Multi-context
flop_count_dp_mul Number of double-precision floating-point multiply operations executed by non-predicated threads Multi-context
flop_count_sp Number of single-precision floating-point operations executed by non-predicated threads (add, multiply, multiply-accumulate and special). Each multiply-accumulate operation contributes 2 to the count. Multi-context
flop_count_sp_add Number of single-precision floating-point add operations executed by non-predicated threads Multi-context
flop_count_sp_fma Number of single-precision floating-point multiply-accumulate operations executed by non-predicated threads. Each multiply-accumulate operation contributes 1 to the count. Multi-context
flop_count_sp_mul Number of single-precision floating-point multiply operations executed by non-predicated threads Multi-context
flop_count_sp_special Number of single-precision floating-point special operations executed by non-predicated threads Multi-context
flop_dp_efficiency Ratio of achieved to peak double-precision floating-point operations Multi-context
flop_sp_efficiency Ratio of achieved to peak single-precision floating-point operations Multi-context
gld_efficiency Ratio of requested global memory load throughput to required global memory load throughput. If the code has surface loads then the metric will report lower values than actual efficiency Multi-context*
gld_requested_throughput Requested global memory load throughput Multi-context
gld_throughput Global memory load throughput Multi-context*
gld_transactions Number of global memory load transactions expressed as percentage Multi-context*
gld_transactions_per_request Average number of global memory load transactions performed for each global memory load. The metric can give higher values than expected if the code has surface loads Multi-context*
global_cache_replay_overhead Average number of replays due to global memory cache misses for each instruction executed Multi-context
global_replay_overhead Average number of replays due to global memory cache misses Multi-context
gst_efficiency Ratio of requested global memory store throughput to required global memory store throughput expressed as percentage Multi-context*
gst_requested_throughput Requested global memory store throughput Multi-context
gst_throughput Global memory store throughput Multi-context*
gst_transactions Number of global memory store transactions Multi-context*
gst_transactions_per_request Average number of global memory store transactions performed for each global memory store. The metric can give higher values than expected if the code has surface stores. Multi-context*
inst_bit_convert Number of bit-conversion instructions executed by non-predicated threads Multi-context
inst_compute_ld_st Number of compute load/store instructions executed by non-predicated threads Multi-context
inst_control Number of control-flow instructions executed by non-predicated threads (jump, branch, etc.) Multi-context
inst_executed The number of instructions executed Multi-context
inst_fp_32 Number of single-precision floating-point instructions executed by non-predicated threads (arithmetric, compare, etc.) Multi-context
inst_fp_64 Number of double-precision floating-point instructions executed by non-predicated threads (arithmetric, compare, etc.) Multi-context
inst_integer Number of integer instructions executed by non-predicated threads Multi-context
inst_inter_thread_communication Number of inter-thread communication instructions executed by non-predicated threads Multi-context
inst_issued The number of instructions issued Multi-context
inst_misc Number of miscellaneous instructions executed by non-predicated threads Multi-context
inst_per_warp Average number of instructions executed by each warp Multi-context
inst_replay_overhead Average number of replays for each instruction executed Multi-context
ipc Instructions executed per cycle Multi-context
ipc_instance Instructions executed per cycle for a single multiprocessor Multi-context
issue_slot_utilization Percentage of issue slots that issued at least one instruction, averaged across all cycles Multi-context
issue_slots The number of issue slots used Multi-context
issued_ipc Instructions issued per cycle Multi-context
l1_cache_global_hit_rate Hit rate in L1 cache for global loads Multi-context*
l1_cache_local_hit_rate Hit rate in L1 cache for local loads and stores Multi-context*
l1_shared_utilization The utilization level of the L1/shared memory relative to peak utilization on a scale of 0 to 10. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
l2_atomic_throughput Memory read throughput seen at L2 cache for atomic and reduction requests Multi-context*
l2_atomic_transactions Memory read transactions seen at L2 cache for atomic and reduction requests Multi-context*
l2_l1_read_hit_rate Hit rate at L2 cache for all read requests from L1 cache. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
l2_l1_read_throughput Memory read throughput seen at L2 cache for read requests from L1 cache. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
l2_l1_read_transactions Memory read transactions seen at L2 cache for all read requests from L1 cache. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
l2_l1_write_throughput Memory write throughput seen at L2 cache for write requests from L1 cache. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
l2_l1_write_transactions Memory write transactions seen at L2 cache for all write requests from L1 cache. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
l2_read_throughput Memory read throughput seen at L2 cache for all read requests Multi-context*
l2_read_transactions Memory read transactions seen at L2 cache for all read requests Multi-context*
l2_tex_read_transactions Memory read transactions seen at L2 cache for read requests from the texture cache Multi-context*
l2_texture_read_hit_rate Hit rate at L2 cache for all read requests from texture cache. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
l2_texture_read_throughput Memory read throughput seen at L2 cache for read requests from the texture cache Multi-context*
l2_utilization The utilization level of the L2 cache relative to the peak utilization on a scale of 0 to 10 Multi-context*
l2_write_throughput Memory write throughput seen at L2 cache for all write requests Multi-context*
l2_write_transactions Memory write transactions seen at L2 cache for all write requests Multi-context*
ldst_executed Number of executed load and store instructions Multi-context
ldst_fu_utilization The utilization level of the multiprocessor function units that execute global, local and shared memory instructions on a scale of 0 to 10 Multi-context
ldst_issued Number of issued load and store instructions Multi-context
local_load_throughput Local memory load throughput Multi-context*
local_load_transactions Number of local memory load transactions Multi-context*
local_load_transactions_per_request Average number of local memory load transactions performed for each local memory load Multi-context*
local_memory_overhead Ratio of local memory traffic to total memory traffic between the L1 and L2 caches expressed as percentage. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
local_replay_overhead Average number of replays due to local memory accesses for each instruction executed Multi-context
local_store_throughput Local memory store throughput Multi-context*
local_store_transactions Number of local memory store transactions Multi-context*
local_store_transactions_per_request Average number of local memory store transactions performed for each local memory store Multi-context*
nc_cache_global_hit_rate Hit rate in non coherent cache for global loads Multi-context*
nc_gld_efficiency Ratio of requested non coherent global memory load throughput to required non coherent global memory load throughput expressed as percentage Multi-context*
nc_gld_requested_throughput Requested throughput for global memory loaded via non-coherent cache Multi-context
nc_gld_throughput Non coherent global memory load throughput Multi-context*
nc_l2_read_throughput Memory read throughput for non coherent global read requests seen at L2 cache Multi-context*
nc_l2_read_transactions Memory read transactions seen at L2 cache for non coherent global read requests Multi-context*
shared_efficiency Ratio of requested shared memory throughput to required shared memory throughput expressed as percentage Multi-context*
shared_load_throughput Shared memory load throughput Multi-context*
shared_load_transactions Number of shared memory load transactions Multi-context*
shared_load_transactions_per_request Average number of shared memory load transactions performed for each shared memory load Multi-context*
shared_replay_overhead Average number of replays due to shared memory conflicts for each instruction executed Multi-context
shared_store_throughput Shared memory store throughput Multi-context*
shared_store_transactions Number of shared memory store transactions Multi-context*
shared_store_transactions_per_request Average number of shared memory store transactions performed for each shared memory store Multi-context*
sm_efficiency The percentage of time at least one warp is active on a multiprocessor averaged over all multiprocessors on the GPU Multi-context*
sm_efficiency_instance The percentage of time at least one warp is active on a specific multiprocessor Multi-context*
stall_constant_memory_dependency Percentage of stalls occurring because of immediate constant cache miss. This is available for compute capability 3.2, 3.5 and 3.7. Multi-context
stall_exec_dependency Percentage of stalls occurring because an input required by the instruction is not yet available Multi-context
stall_inst_fetch Percentage of stalls occurring because the next assembly instruction has not yet been fetched Multi-context
stall_memory_dependency Percentage of stalls occurring because a memory operation cannot be performed due to the required resources not being available or fully utilized, or because too many requests of a given type are outstanding. Multi-context
stall_memory_throttle Percentage of stalls occurring because of memory throttle. Multi-context
stall_not_selected Percentage of stalls occurring because warp was not selected. Multi-context
stall_other Percentage of stalls occurring due to miscellaneous reasons Multi-context
stall_pipe_busy Percentage of stalls occurring because a compute operation cannot be performed due to the required resources not being available. This is available for compute capability 3.2, 3.5 and 3.7. Multi-context
stall_sync Percentage of stalls occurring because the warp is blocked at a __syncthreads() call Multi-context
stall_texture Percentage of stalls occurring because the texture sub-system is fully utilized or has too many outstanding requests Multi-context
sysmem_read_throughput System memory read throughput. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
sysmem_read_transactions System memory read transactions. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
sysmem_utilization The utilization level of the system memory relative to the peak utilization on a scale of 0 to 10. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
sysmem_write_throughput System memory write throughput. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
sysmem_write_transactions System memory write transactions. This is available for compute capability 3.0, 3.5 and 3.7. Multi-context*
tex_cache_hit_rate Texture cache hit rate Multi-context*
tex_cache_throughput Texture cache throughput Multi-context*
tex_cache_transactions Texture cache read transactions Multi-context*
tex_fu_utilization The utilization level of the multiprocessor function units that execute texture instructions on a scale of 0 to 10 Multi-context
tex_utilization The utilization level of the texture cache relative to the peak utilization on a scale of 0 to 10 Multi-context*
warp_execution_efficiency Ratio of the average active threads per warp to the maximum number of threads per warp supported on a multiprocessor expressed as percentage Multi-context
warp_nonpred_execution_efficiency Ratio of the average active threads per warp executing non-predicated instructions to the maximum number of threads per warp supported on a multiprocessor expressed as percentage Multi-context

* The multi-context scope is supported for devices with compute capability 3.0, 3.5 and 3.7.

Metric Reference - Compute Capability 5.x

Devices with compute capability greater than or equal to 5.0 implement the metrics shown in the following table. A scope value of single-context indicates that the metric can only be accurately collected when a single context (CUDA or graphics) is executing on the GPU. A scope value of multi-context indicates that the metric can be accurately collected when multiple contexts are executing on the GPU. Note that, starting CUDA Toolkit 7.0 all metrics can be collected accurately on the devices with compute capability 5.0 when multiple contexts are executing on the GPU. Note that for some metrics the multi-context scope is supported only for specific devices. Such metrics are marked with "Multi-context*" under the "Scope" column. Refer the note at the bottom of the table.

Table 3. Capability 5.x Metrics
Metric Name Description Scope
achieved_occupancy Ratio of the average active warps per active cycle to the maximum number of warps supported on a multiprocessor Multi-context
atomic_transactions Global memory atomic and reduction transactions Multi-context
atomic_transactions_per_request Average number of global memory atomic and reduction transactions performed for each atomic and reduction instruction Multi-context
branch_efficiency Ratio of non-divergent branches to total branches expressed as percentage Multi-context
cf_executed Number of executed control-flow instructions Multi-context
cf_fu_utilization The utilization level of the multiprocessor function units that execute control-flow instructions on a scale of 0 to 10 Multi-context
cf_issued Number of issued control-flow instructions Multi-context
double_precision_fu_utilization The utilization level of the multiprocessor function units that execute double-precision floating-point instructions and integer instructions on a scale of 0 to 10 Multi-context
dram_read_throughput Device memory read throughput Multi-context*
dram_read_transactions Device memory read transactions Multi-context*
dram_utilization The utilization level of the device memory relative to the peak utilization on a scale of 0 to 10 Multi-context*
dram_write_throughput Device memory write throughput Multi-context*
dram_write_transactions Device memory write transactions Multi-context*
ecc_throughput ECC throughput from L2 to DRAM Multi-context*
ecc_transactions Number of ECC transactions between L2 and DRAM Multi-context*
eligible_warps_per_cycle Average number of warps that are eligible to issue per active cycle Multi-context
flop_count_dp Number of double-precision floating-point operations executed by non-predicated threads (add, multiply, multiply-accumulate and special). Each multiply-accumulate operation contributes 2 to the count. Multi-context
flop_count_dp_add Number of double-precision floating-point add operations executed by non-predicated threads Multi-context
flop_count_dp_fma Number of double-precision floating-point multiply-accumulate operations executed by non-predicated threads. Each multiply-accumulate operation contributes 1 to the count. Multi-context
flop_count_dp_mul Number of double-precision floating-point multiply operations executed by non-predicated threads Multi-context
flop_count_sp Number of single-precision floating-point operations executed by non-predicated threads (add, multiply, multiply-accumulate and special). Each multiply-accumulate operation contributes 2 to the count. Multi-context
flop_count_sp_add Number of single-precision floating-point add operations executed by non-predicated threads Multi-context
flop_count_sp_fma Number of single-precision floating-point multiply-accumulate operations executed by non-predicated threads. Each multiply-accumulate operation contributes 1 to the count. Multi-context
flop_count_sp_mul Number of single-precision floating-point multiply operations executed by non-predicated threads Multi-context
flop_count_sp_special Number of single-precision floating-point special operations executed by non-predicated threads Multi-context
flop_dp_efficiency Ratio of achieved to peak double-precision floating-point operations Multi-context
flop_sp_efficiency Ratio of achieved to peak single-precision floating-point operations Multi-context
gld_efficiency Ratio of requested global memory load throughput to required global memory load throughput expressed as percentage Multi-context*
gld_requested_throughput Requested global memory load throughput Multi-context
gld_throughput Global memory load throughput Multi-context*
gld_transactions Number of global memory load transactions Multi-context*
gld_transactions_per_request Average number of global memory load transactions performed for each global memory load Multi-context*
global_hit_rate Hit rate for global loads Multi-context*
gst_efficiency Ratio of requested global memory store throughput to required global memory store throughput expressed as percentage Multi-context*
gst_requested_throughput Requested global memory store throughput Multi-context
gst_throughput Global memory store throughput Multi-context*
gst_transactions Number of global memory store transactions Multi-context*
gst_transactions_per_request Average number of global memory store transactions performed for each global memory store Multi-context*
inst_bit_convert Number of bit-conversion instructions executed by non-predicated threads Multi-context
inst_compute_ld_st Number of compute load/store instructions executed by non-predicated threads Multi-context
inst_control Number of control-flow instructions executed by non-predicated threads (jump, branch, etc.) Multi-context
inst_executed The number of instructions executed Multi-context
inst_fp_32 Number of single-precision floating-point instructions executed by non-predicated threads (arithmetric, compare, etc.) Multi-context
inst_fp_64 Number of double-precision floating-point instructions executed by non-predicated threads (arithmetric, compare, etc.) Multi-context
inst_integer Number of integer instructions executed by non-predicated threads Multi-context
inst_inter_thread_communication Number of inter-thread communication instructions executed by non-predicated threads Multi-context
inst_issued The number of instructions issued Multi-context
inst_misc Number of miscellaneous instructions executed by non-predicated threads Multi-context
inst_per_warp Average number of instructions executed by each warp Multi-context
inst_replay_overhead Average number of replays for each instruction executed Multi-context
ipc Instructions executed per cycle Multi-context
issue_slot_utilization Percentage of issue slots that issued at least one instruction, averaged across all cycles Multi-context
issue_slots The number of issue slots used Multi-context
issued_ipc Instructions issued per cycle Multi-context
l2_atomic_throughput Memory read throughput seen at L2 cache for atomic and reduction requests Multi-context
l2_atomic_transactions Memory read transactions seen at L2 cache for atomic and reduction requests Multi-context*
l2_read_throughput Memory read throughput seen at L2 cache for all read requests Multi-context*
l2_read_transactions Memory read transactions seen at L2 cache for all read requests Multi-context*
l2_tex_read_hit_rate Hit rate at L2 cache for all read requests from texture cache Multi-context*
l2_tex_read_throughput Memory read throughput seen at L2 cache for read requests from the texture cache Multi-context*
l2_tex_read_transactions Memory read transactions seen at L2 cache for read requests from the texture cache Multi-context*
l2_tex_write_hit_rate Hit Rate at L2 cache for all write requests from texture cache Multi-context*
l2_tex_write_throughput Memory write throughput seen at L2 cache for write requests from the texture cache Multi-context*
l2_tex_write_transactions Memory write transactions seen at L2 cache for write requests from the texture cache Multi-context*
l2_utilization The utilization level of the L2 cache relative to the peak utilization on a scale of 0 to 10 Multi-context*
l2_write_throughput Memory write throughput seen at L2 cache for all write requests Multi-context*
l2_write_transactions Memory write transactions seen at L2 cache for all write requests Multi-context*
ldst_executed Number of executed load and store instructions Multi-context
ldst_fu_utilization The utilization level of the multiprocessor function units that execute global, local and shared memory instructions on a scale of 0 to 10 Multi-context
ldst_issued Number of issued load and store instructions Multi-context
local_hit_rate Hit rate for local loads and stores Multi-context*
local_load_throughput Local memory load throughput Multi-context*
local_load_transactions Number of local memory load transactions Multi-context*
local_load_transactions_per_request Average number of local memory load transactions performed for each local memory load Multi-context*
local_memory_overhead Ratio of local memory traffic to total memory traffic between the L1 and L2 caches expressed as percentage Multi-context*
local_store_throughput Local memory store throughput Multi-context*
local_store_transactions Number of local memory store transactions Multi-context*
local_store_transactions_per_request Average number of local memory store transactions performed for each local memory store Multi-context*
shared_efficiency Ratio of requested shared memory throughput to required shared memory throughput expressed as percentage Multi-context*
shared_load_throughput Shared memory load throughput Multi-context*
shared_load_transactions Number of shared memory load transactions Multi-context*
shared_load_transactions_per_request Average number of shared memory load transactions performed for each shared memory load Multi-context*
shared_store_throughput Shared memory store throughput Multi-context*
shared_store_transactions Number of shared memory store transactions Multi-context*
shared_store_transactions_per_request Average number of shared memory store transactions performed for each shared memory store Multi-context*
shared_utilization The utilization level of the shared memory relative to peak utilization on a scale of 0 to 10 Multi-context*
single_precision_fu_utilization The utilization level of the multiprocessor function units that execute single-precision floating-point instructions and integer instructions on a scale of 0 to 10 Multi-context
sm_efficiency The percentage of time at least one warp is active on a multiprocessor Multi-context*
special_fu_utilization The utilization level of the multiprocessor function units that execute sin, cos, ex2, popc, flo, and similar instructions on a scale of 0 to 10 Multi-context
stall_constant_memory_dependency Percentage of stalls occurring because of immediate constant cache miss Multi-context
stall_exec_dependency Percentage of stalls occurring because an input required by the instruction is not yet available Multi-context
stall_inst_fetch Percentage of stalls occurring because the next assembly instruction has not yet been fetched Multi-context
stall_memory_dependency Percentage of stalls occurring because a memory operation cannot be performed due to the required resources not being available or fully utilized, or because too many requests of a given type are outstanding Multi-context
stall_memory_throttle Percentage of stalls occurring because of memory throttle Multi-context
stall_not_selected Percentage of stalls occurring because warp was not selected Multi-context
stall_other Percentage of stalls occurring due to miscellaneous reasons Multi-context
stall_pipe_busy Percentage of stalls occurring because a compute operation cannot be performed due to the required resources not being available Multi-context
stall_sync Percentage of stalls occurring because the warp is blocked at a __syncthreads() call Multi-context
stall_texture Percentage of stalls occurring because the texture sub-system is fully utilized or has too many outstanding requests Multi-context
sysmem_read_throughput System memory read throughput Multi-context*
sysmem_read_transactions System memory read transactions Multi-context*
sysmem_utilization The utilization level of the system memory relative to the peak utilization on a scale of 0 to 10 Multi-context*
sysmem_write_throughput System memory write throughput Multi-context*
sysmem_write_transactions System memory write transactions Multi-context*
tex_cache_hit_rate Texture cache hit rate Multi-context*
tex_cache_throughput Texture cache throughput Multi-context*
tex_cache_transactions Texture cache read transactions Multi-context*
tex_fu_utilization The utilization level of the multiprocessor function units that execute texture instructions on a scale of 0 to 10 Multi-context
tex_utilization The utilization level of the texture cache relative to the peak utilization on a scale of 0 to 10 Multi-context*
warp_execution_efficiency Ratio of the average active threads per warp to the maximum number of threads per warp supported on a multiprocessor expressed as percentage Multi-context
warp_nonpred_execution_efficiency Ratio of the average active threads per warp executing non-predicated instructions to the maximum number of threads per warp supported on a multiprocessor Multi-context

* The Multi-context scope for this metric is supported only for devices with compute capability 5.0 and 5.2.

1.7. Samples

The CUPTI installation includes several samples that demonstrate the use of the CUPTI APIs. The samples are:

activity_trace_async
This sample shows how to collect a trace of CPU and GPU activity using the new asynchronous activity buffer APIs.
callback_event
This sample shows how to use both the callback and event APIs to record the events that occur during the execution of a simple kernel. The sample shows the required ordering for synchronization, and for event group enabling, disabling and reading.
callback_metric
This sample shows how to use both the callback and metric APIs to record the metric's events during the execution of a simple kernel, and then use those events to calculate the metric value.
callback_timestamp
This sample shows how to use the callback API to record a trace of API start and stop times.
cupti_query
This sample shows how to query CUDA-enabled devices for their event domains, events, and metrics.
event_sampling
This sample shows how to use the event APIs to sample events using a separate host thread.
event_multi_gpu
This sample shows how to use the CUPTI event and CUDA APIs to sample events on a setup with multiple GPUs. The sample shows the required ordering for synchronization, and for event group enabling, disabling and reading.
sass_source_map
This sample shows how to generate CUpti_ActivityInstructionExecution records and how to map SASS assembly instructions to CUDA C source.
unified_memory
This sample shows how to collect information about page transfers for unified memory.
pc_sampling
This sample shows how to collect PC Sampling profiling information for a kernel.