ROCProfiler library specification#
The goal of the implementation is to provide a hardware-specific low-level performance analysis interface for profiling of GPU compute applications. The profiling includes hardware performance counters with complex performance metrics and hardware traces. Performance counters are treated as the basic metrics, and formulas can be defined to derive more complex metrics.
The library can be loaded by HSA runtime as a tool plugin and it can be loaded by higher level hardware-independent performance analysis API like PAPI. The library provides a C API and is based on AQL profile AMD-specific HSA extensions. The library features are listed here:
Provides methods to query the list of supported hardware features.
Provides profiling APIs to start, stop, and read metrics results and tracing data.
Provides intercepting API for collecting per-kernel profiling data for the kernels dispatched to HSA AQL queues.
Provides mechanism to load profiling tool library plugin by env variable
ROCP_TOOL_LIB.Responsible for allocation of the buffers for profiling and notifying about output data buffer overflow for traces.
Implemented based on AMD-specific AQLprofile HSA extension.
Implementation is abstracted from the specific GFXIP.
Implementation is extensible:
Easy addition of counters and metrics
Counters enumeration
Counters and metrics can be dynamically configured using XML configuration files (
metrics.xml) with counters and metrics tables:Counters table entry, basic metric: counter name, block name, event id
Complex metrics table entry: metric name, an expression for calculating the metric from the counters
Metrics XML file example:
<gfx8>
<metric name=L1_CYCLES_COUNTER block=L1 event=0 >
<metric name=L1_MISS_COUNTER block=L1 event=33 >
...
</gfx8>
<gfx9>
...
</gfx9>
<global>
<metric name=L1_MISS_RATIO expr=L1_CYCLES_COUNT/ L1_MISS_COUNTER ></metric>
</global>
Environment variables#
HSA_TOOLS_LIB- required to be set to the name of rocprofiler library to be loaded by HSA runtimeROCP_METRICS- path to the metrics XML fileROCP_TOOL_LIB- path to profiling tool library loaded by ROCProfilerROCP_HSA_INTERCEPT- if set then HSA dispatches intercepting is enabled
Logging#
Set the following environment variables to enable logging:
Environment Variable |
Purpose |
Log Files |
|---|---|---|
|
Enables error message logging |
|
|
Enables verbose tracing |
|
General API#
The library supports a method for getting the error number and error string of the last failed library API call. To check the conformance of the library API header, and the library binary, the following version macros and API methods can be used.
Returning the error and error string methods:
rocprofiler_error_string- method for returning the error string
Library version:
ROCPROFILER_VERSION_MAJOR- API major version macroROCPROFILER_VERSION_MINOR- API minor version macrorocprofiler_version_major- library major versionrocprofiler_version_minor- library minor version
Returning the error and error string methods#
const char* rocprofiler_error_string();
Library version#
The library provides back compatibility if the library major version is less or equal then the API major version macro.
API version macros defined in the library API header rocprofiler.h:
ROCPROFILER_VERSION_MAJOR
ROCPROFILER_VERSION_MINOR
Methods to check library major and minor versions:
uint32_t rocprofiler_major_version();
uint32_t rocprofiler_minor_version();
Backend API#
The library provides methods to open/close a profiling context, to start, stop, and read HW performance counters and traces, to intercept kernel dispatches to collect per-kernel profiling data. The library also provides methods to calculate complex performance metrics and to query the list of available metrics.
The library distinguishes two profiling features, metrics and traces, where HW performance counters are treated as the basic metrics. To check if there was an error the library methods return HSA standard status code. For a given context the profiling can be started/stopped and counters sampled in standalone mode or profiling can be initiated by intercepting the kernel dispatches with registering a dispatch callback.
For counters sampling, which is the usage model of higher level APIs like PAPI, the start/stop/read APIs should be used. To collect per-kernel data for kernels submitted to HSA queues, the dispatch callback API should be used.
The library provides back compatibility if the library major version is less or equal.
Returned API status:
hsa_status_t- HSA status codes are used from hsa.h header
Loading and Configuring, loadable plugin on-load/unload methods:
rocprofiler_settings_t- global propertiesOnLoadToolOnLoadToolPropOnUnloadTool
Info API:
rocprofiler_info_kind_t- profiling info kindrocprofiler_info_query_t- profiling info queryrocprofiler_info_data_t- profiling info datarocprofiler_get_info- return the info for a given info kindrocprofiler_iterote_inf_- iterate over the info for a given info kindrocprofiler_query_info- iterate over the info for a given info query
Context API:
rocprofiler_t- profiling context handlerocprofiler_feature_kind_t- profiling feature kindrocprofiler_feature_parameter_t- profiling feature parameterrocprofiler_data_kind_t- profiling data kindrocprofiler_data_t- profiling datarocprofiler_feature_t- profiling featurerocprofiler_mode_t- profiling modesrocprofiler_properties_t- profiler propertiesrocprofiler_open- open new profiling contextrocprofiler_close- close profiling context and release all allocated resourcesrocprofiler_group_count- return profiling groups countrocprofiler_get_group- return profiling group for a given indexrocprofiler_get_metrics- method for calculating the metrics datarocprofiler_iterate_trace_data- method for iterating output trace data instancesrocprofiler_time_id_t- supported time value ID enumerationrocprofiler_get_time- return time for a given time ID and profiling timestamp value
Sampling API:
rocprofiler_start- start profilingrocprofiler_stop- stop profilingrocprofiler_read- read profiling data to the profiling features objectsrocprofiler_get_data- wait for profiling dataGroup versions of start/stop/read/get_data methods:
rocprofiler_group_startrocprofiler_group_stoprocprofiler_group_readrocprofiler_group_get_data
Intercepting API:
rocprofiler_callback_t- profiling callback typerocprofiler_callback_data_t- profiling callback data typerocprofiler_dispatch_record_t- dispatch recordrocprofiler_queue_callbacks_t- queue callbacks, dispatch/destroyrocprofiler_set_queue_callbacks- set queue kernel dispatch and queue destroy callbacksrocprofiler_remove_queue_callbacks- remove queue callbacks
Context pool API:
rocprofiler_pool_t- context pool handlerocprofiler_pool_entry_t- context pool entryrocprofiler_pool_properties_t- context pool propertiesrocprofiler_pool_handler_t- context pool completion handlerrocprofiler_pool_open- context pool openrocprofiler_pool_close- context pool closerocprofiler_pool_fetch- fetch and empty context entry to poolrocprofiler_pool_release- release a context entryrocprofiler_pool_iterate- iterated fetched context entriesrocprofiler_pool_flush- flush completed context entries
Loading and configuring#
The profiling properties can be set by profiler plugin on loading by ROC runtime.
The profiler library plugin can be set by ROCP_TOOL_LIB env var.
Global properties:
typedef struct {
uint32_t intercept_mode;
uint64_t timeout;
uint32_t timestamp_on;
} rocprofiler_settings_t;
On load/unload methods defined in profiling tool library loaded by ROCP_TOOL_LIB env var:
extern "C" void OnLoadTool();
extern "C" void OnLoadToolProp(rocprofiler_settings_t* settings);
extern "C" void OnUnloadTool();
Info API#
The profiling metrics are defined by name and the traces are defined by name and parameters.
All supported features can be iterated using iterate_info/query_info methods. The counter
names are defined in counters table configuration file, each counter has a unique name and
defined by block name and event ID. The traces and trace parameters names are the same as in
the hardware documentation and the parameters codes are rocprofiler_feature_parameter_t values,
see below in the Context API section.
Profiling info kind:
typedef enum {
ROCPROFILER_INFO_KIND_METRIC = 0, // metric info
ROCPROFILER_INFO_KIND_METRIC_COUNT = 1, // metrics count
ROCPROFILER_INFO_KIND_TRACE = 2, // trace info
ROCPROFILER_INFO_KIND_TRACE_COUNT = 3, // traces count
} rocprofiler_info_kind_t;
Profiling info data:
typedef struct {
rocprofiler_info_kind_t kind; // info data kind
union {
struct {
const char* name; // metric name
uint32_t instances; // instances number
const char* expr; // metric expression, NULL for basic counters
const char* description; // metric description
const char* block_name; // block name
uint32_t block_counters; // number of block counters
} metric;
struct {
const char* name; // trace name
const char* description; // trace description
uint32_t parameter_count; // supported by the trace number
// parameters
} trace;
};
} rocprofiler_info_data_t;
Return info for a given info kind:
has_status_t rocprofiler_get_info(
const hsa_agent_t* agent, // [in] GPU handle, NULL for all
// GPU agents
rocprofiler info_kind_t kind, // kind of iterated info
void *data); // data passed to callback
Iterate over the info for a given info kind, and invoke an application-defined callback on every iteration:
has_status_t rocprofiler_iterate_info(
const hsa_agent_t* agent, // [in] GPU handle, NULL for all
// GPU agents
rocprofiler info_kind_t kind, // kind of iterated info
hsa_status_t (*callback)(const rocprofiler_info_data_t info, void *data), // callback
void *data);
Iterate over the info for a given info query, and invoke an application-defined callback on every iteration. The query fields set to NULL define the query wildcard:
has_status_t rocprofiler_query_info(
const hsa_agent_t* agent, // [in] GPU handle, NULL for all
// GPU agents
rocprofiler info_kind_t kind, // kind of iterated info
rocprofiler_info_data_t query, // info query
hsa_status_t (*callback)(const rocprofiler_info_data_t info, void *data), // callback
void *data); // data passed to callback
Context API#
Profiling context is accumulating all profiling information including profiling features which carry profiling data, required buffers for profiling command packets and output data. The context can be created and deleted by the library open/close methods.
By deleting the context all accumulated by the library resources associated with this context will be released. If more than one run is required to collect all requested counters data, then data for all profiling groups should be collected first, then the metrics can be calculated by loading the saved groups’ data to the profiling context. Saving and loading of the groups data is responsibility of the tool. The groups are automatically identified on the profiling context open and there is API to access them, as shown in the Profiling Groups example that follows.
Profiling context handle:
typename rocprofiler_t;
Profiling feature kind:
typedef enum {
ROCPROFILER_FEATURE_KIND_METRIC = 0, // metric
ROCPROFILER_FEATURE_KIND_TRACE = 1 // trace
} rocprofiler_feature_kind_t;
Profiling feature parameter:
typedef hsa_ven_amd_aqlprofile_parameter_t rocprofiler_feature_parameter_t;
Profiling data kind:
typedef enum {
ROCPROFILER_DATA_KIND_UNINIT = 0, // data uninitialized
ROCPROFILER_DATA_KIND_INT32 = 1, // 32bit integer
ROCPROFILER_DATA_KIND_INT64 = 2, // 64bit integer
ROCPROFILER_DATA_KIND_FLOAT = 3, // float single-precision result
ROCPROFILER_DATA_KIND_DOUBLE = 4, // float double-precision result
ROCPROFILER_DATA_KIND_BYTES = 5 // trace output as a bytes array
} rocprofiler_data_kind_t;
Profiling data:
typedef struct {
rocprofiler_data_kind_t kind; // result kind
union {
uint32_t result_int32; // 32bit integer result
uint64_t result_int64; // 64bit integer result
float result_float; // float single-precision result
double result_double; // float double-precision result
typedef struct {
void* ptr; // pointer
uint32_t size; // byte size
uint32_t instances; // number of trace instances
} result_bytes; // data by ptr and byte size
};
} rocprofiler_data_t;
Profiling feature:
typedef struct {
rocprofiler_feature_kind_t type; // feature type
const char* name; // feature name
const rocprofiler_feature_parameter_t* parameters; // feature parameters
uint32_t parameter_count; // feature parameter count
rocprofiler_data_t* data; // profiling data
} rocprofiler_feature_t;
Profiling mode masks:
There are several modes which can be specified for the profiling context.
STANDALONE mode can be used for the counters sampling in another then application context
to support statistical system-wide profiling. In this mode the profiling context supports
its own queue which can be created on the context open if the CREATEQUEUE mode is also specified.
See the Profiler Properties example that follows for the standalone mode queue properties.
The profiler supports several profiling groups for collecting profiling data in several
runs and SINGLEGROUP mode allows only one group and the context open will fail if more
groups are needed.
typedef enum {
ROCPROFILER_MODE_STANDALONE = 1, // standalone mode when ROC profiler
// supports own AQL queue
ROCPROFILER_MODE_CREATEQUEUE = 2, // profiler creates queue in STANDALONE mode
ROCPROFILER_MODE_SINGLEGROUP = 4 // profiler allows one group only and fails
// if more groups are needed
} rocprofiler_mode_t;
Context data readiness callback:
typedef void (*rocprofiler_context_callback_t)(
rocprofiler_group_t* group, // profiling group
void* arg); // callback arg
Profiler properties:
There are several properties which can be specified for the context. A callback can be
registered which will be called when the context data is ready. In standalone profiling mode
ROCPROFILER_MODE_STANDALONE the context supports its own queue, and the queue can be set by
the property queue or a queue will be created with the specified depth queue_depth if mode
ROCPROFILER_MODE_CREATEQUEUE is also specified.
typedef struct {
rocprofiler_context_callback_t callback; // callback on the context data readiness
void* callback_arg; // callback arg
has_queue_t* queue; // HSA queue for standalone mode
uint32_t queue_depth; // created queue depth, for create-queue mode
} rocprofiler_properties_t;
Open/close profiling context:
hsa_status_t rocprofiler_open(
hsa_agent_t agent, // GPU handle
rocprofiler_feature_t* features, // [in/out] profiling feature array
uint32_t feature_count, // profiling feature count
rocprofiler_t** context, // [out] profiling context handle
uint32_t mode, // profiling mode mask
rocprofiler_properties_t* properties); // profiler properties
hsa_status_t rocprofiler_close(
rocprofiler_t* context); // [in] profiling context
Profiling groups:
The profiler on the context open automatically identifies a required number of the application runs to collect all data needed for all specified metrics, and creates a metric group for each run. Data for all profiling groups should be collected, and then the metrics can be calculated by loading the saved groups’ data to the profiling context. Saving and loading of the groups data is the responsibility of the tool.
typedef struct {
uint32_t index; // profiling group index
rocprofiler_feature_t** features; // profiling features array
uint32_t feature_count; // profiling feature count
rocprofiler_t* context; // profiling context handle
} rocprofiler_group_t;
Return profiling groups count:
hsa_status_t rocprofiler_group_count(
rocprofiler_t* context); // [in/out] profiling context
uint32* count); // [out] profiling groups count
Return the profiling group for a given index:
hsa_status_t rocprofiler_get_group(
rocprofiler_t* context, // [in/out] profiling context,
// will be returned as
// a part of the group structure
uint32_t index, // [in] group index
rocprofiler_group_t* group); // [out] profiling group
Calculate metrics data. The data will be stored to the registered profiling features data fields.
After all profiling context data is ready the registered metrics can be calculated. The context
data readiness can be checked by get_data API or using the context callback.
hsa_status_t rocprofiler_get_metrics(
rocprofiler_t* context); // [in/out] profiling context
Method for iterating trace data instances:
Trace data can have several instances, for example, one instance per Shader Engine.
hsa_status_t rocprofiler_iterate_trace_data(
const rocprofiler_t* contex, // [in] context object
hsa_ven_amd_aqlprofile_data_callback_t callback, // [in] callback to iterate
// the output data
void* callback_data); // [in/out] passed to callback data
Converting of profiling timestamp to time value for supported time ID.
Supported time value ID enumeration:
typedef enum {
ROCPROFILER_TIME_ID_CLOCK_REALTIME = 0, // Linux realtime clock time
ROCPROFILER_TIME_ID_CLOCK_MONOTONIC = 1, // Linux monotonic clock time
} rocprofiler_time_id_t;
Method for converting of profiling timestamp to time value for a given time ID:
hsa_status_t rocprofiler_get_time(
rocprofiler_time_id_t time_id, // identifier of the particular
// time to convert the timestamp
uint64_t timestamp, // profiling timestamp
uint64_t* value_ns); // [out] returned time ‘ns’ value
Sampling API#
The API supports the counters sampling usage model with start/read/stop methods and also lets to wait for the profiling data in the intercepting usage model with get_data method.
Start/stop/read methods:
hsa_status_t rocprofiler_start(
rocprofiler_t* context, // [in/out] profiling context
uint32_t group_index = 0); // group index
hsa_status_t rocprofiler_stop(
rocprofiler_t* context, // [in/out] profiling context
uint32_t group_index = 0); // group index
hsa_status_t rocprofiler_read(
rocprofiler_t* context, // [in/out] profiling context
uint32_t group_index = 0); // group index
Wait for profiling data:
hsa_status_t rocprofiler_get_data(
rocprofiler_t* context, // [in/out] profiling context
uint32_t group_index = 0); // group index
Group versions of the above start/stop/read/get_data methods:
hsa_status_t rocprofiler_group_start(
rocprofiler_group_t* group); // [in/out] profiling group
hsa_status_t rocprofiler_group_stop(
rocprofiler_group_t* group); // [in/out] profiling group
hsa_status_t rocprofiler_group_read(
rocprofiler_group_t* group); // [in/out] profiling group
hsa_status_t rocprofiler_group_get_data(
rocprofiler_group_t* group); // [in/out] profiling group
Intercepting API#
The library provides a callback API for enabling profiling for the kernels dispatched to HSA AQL queues. The API enables per-kernel profiling data collection. Currently implemented the option with serializing the kernels execution.
ROC profiler callback type:
hsa_status_t (*rocprofiler_callback_t)(
const rocprofiler_callback_data_t* callback_data, // callback data passed by HSA runtime
void* user_data, // [in/out] user data passed
// to the callback
rocprofiler_group** group); // [out] returned profiling group
Profiling callback data:
typedef struct {
uint64_t dispatch; // dispatch timestamp
uint64_t begin; // begin timestamp
uint64_t end; // end timestamp
uint64_t complete; // completion signal timestamp
} rocprofiler_dispatch_record_t;
typedef struct {
hsa_agent_t agent; // GPU agent handle
uint32_t agent_index; // GPU index
const hsa_queue_t* queue; // HSA queue
uint64_t queue_index; // Index in the queue
const hsa_kernel_dispatch_packet_t* packet; // HSA dispatch packet
const char* kernel_name; // Kernel name
const rocprofiler_dispatch_record_t* record; // Dispatch record
} rocprofiler_callback_data_t;
Queue callbacks:
typedef struct {
rocprofiler_callback_t dispatch; // kernel dispatch callback
hsa_status_t (*destroy)(hsa_queue_t* queue, void* data); // queue destroy callback
} rocprofiler_queue_callbacks_t;
Adding/removing kernel dispatch and queue destroy callbacks:
hsa_status_t rocprofiler_set_intercepting(
rocprofiler_intercepting_t callbacks, // intercepting callbacks
void* data); // [in/out] passed callbacks data
hsa_status_t rocprofiler_remove_intercepting();
Profiling context pools#
The API provide capability to create a context pool for a given agent and a set of features, to fetch/release a context entry, to register a callback for pool’s contexts completion. Profiling pool handle:
typename rocprofiler_pool_t;
Profiling pool entry:
typedef struct {
rocprofiler_t* context; // context object
void* payload; // payload data object
} rocprofiler_pool_entry_t;
Profiling handler, calling on profiling completion:
typedef bool (*rocprofiler_pool_handler_t)(const rocprofiler_pool_entry_t* entry, void* arg);
Profiling properties:
typedef struct {
uint32_t num_entries; // pool size entries
uint32_t payload_bytes; // payload size bytes
rocprofiler_pool_handler_t handler; // handler on context completion
void* handler_arg; // the handler arg
} rocprofiler_pool_properties_t;
Open profiling pool:
hsa_status_t rocprofiler_pool_open(
hsa_agent_t agent, // GPU handle
rocprofiler_feature_t* features, // [in] profiling features array
uint32_t feature_count, // profiling info count
rocprofiler_pool_t** pool, // [out] context object
uint32_t mode, // profiling mode mask
rocprofiler_pool_properties_t*); // pool properties
Close profiling pool:
hsa_status_t rocprofiler_pool_close(
rocprofiler_pool_t* pool); // profiling pool handle
Fetch profiling pool entry:
hsa_status_t rocprofiler_pool_fetch(
rocprofiler_pool_t* pool, // profiling pool handle
rocprofiler_pool_entry_t* entry); // [out] empty profiling pool entry
Release profiling pool entry:
hsa_status_t rocprofiler_pool_release(
rocprofiler_pool_entry_t* entry); // released profiling pool entry
Iterate fetched profiling pool entries:
hsa_status_t rocprofiler_pool_iterate(
rocprofiler_pool_t* pool, // profiling pool handle
hsa_status_t (*callback)(rocprofiler_pool_entry_t* entry, void* data),
// callback
void *data); // [in/out] data passed to callback
Flush completed entries in profiling pool:
hsa_status_t rocprofiler_pool_flush(
rocprofiler_pool_t* pool); // profiling pool handle
Application code examples#
Querying available metrics#
Info data callback:
hsa_status_t info_data_callback(const rocprofiler_info_data_t info, void *data) {
switch (info.kind) {
case ROCPROFILER_INFO_KIND_METRIC: {
if (info.metric.expr != NULL) {
fprintf(stdout, "Derived counter: gpu-agent%d : %s : %s\n",
info.agent_index, info.metric.name, info.metric.description);
fprintf(stdout, " %s = %s\n", info.metric.name, info.metric.expr);
} else {
fprintf(stdout, "Basic counter: gpu-agent%d : %s",
info.agent_index, info.metric.name);
if (info.metric.instances > 1) {
fprintf(stdout, "[0-%u]", info.metric.instances - 1);
}
fprintf(stdout, " : %s\n", info.metric.description);
fprintf(stdout, " block %s has %u counters\n",
info.metric.block_name, info.metric.block_counters);
}
fflush(stdout);
break;
}
default:
printf("wrong info kind %u\n", kind);
return HSA_STATUS_ERROR;
}
return HSA_STATUS_SUCCESS;
}
Printing all available metrics:
hsa_status_t status = rocprofiler_iterate_info(
agent,
ROCPROFILER_INFO_KIND_METRIC,
info_data_callback,
NULL);
<check status>
Profiling code example#
Profiling of L1 miss ratio, average memory bandwidth
In the example below rocprofiler_group_get_data group APIs are used for the purpose of a usage
example but in SINGLEGROUP mode when only one group is allowed the context handle itself can be
saved and then direct context method rocprofiler_get_data with default group index equal to 0
can be used.
hsa_status_t dispatch_callback(
const rocprofiler_callback_data_t* callback_data,
void* user_data,
rocprofiler_group_t* group)
{
hsa_status_t status = HSA_STATUS_SUCCESS;
// Profiling context
rocprofiler_t* context;
// Profiling info objects
rocprofiler_feature_t features* = new rocprofiler_feature_t[2];
// Tracing parameters
rocprofiler_feature_parameter_t* parameters = new rocprofiler_feature_parameter_t[2];
// Setting profiling features
features[0].type = ROCPROFILER_METRIC;
features[0].name = "L1_MISS_RATIO";
features[1].type = ROCPROFILER_METRIC;
features[1].name = "DRAM_BANDWIDTH";
// Creating profiling context
status = rocprofiler_open(callback_data->dispatch.agent, features, 2, &context,
ROCPROFILER_MODE_SINGLEGROUP, NULL);
<check status>
// Get the profiling group
// For general case with many groups there is rocprofiler_group_count() API
const uint32_t group_index = 0
status = rocprofiler_get_group(context, group_index, group);
<check_status>
// In SINGLEGROUP mode the context handle itself can be saved, because there is just one group
<saving the callback data/profiling group/profiling features>
return status;
}
Profiling tool constructor is adding the dispatch callback:
void profiling_libary_constructor() {
// Defining callback data, no data in this simple example
void* callback_data = NULL;
// Adding observers
hsa_sttaus_t status = rocprofiler_add_dispatch_callback(dispatch_callback, callback_data);
<check status>
// Dispatching profiled kernel
<dispatching profiled kernels>
}
void profiling_libary_destructor() {
<for entry : <saved callbacks data>> {
// In SINGLEGROUP mode the rocprofiler_get_group() method with default zero group
// index can be used, if context handle would be saved
status = rocprofiler_group_get_data(entry->group);
<check status>
status = rocprofiler_get_metrics(entry->group->context);
<check status>
status = rocprofiler_close(entry->group->context);
<check status>
<tool_dump_data_method(entry->dispatch_data, entry->features, entry->features_count)>;
}
}
Option to use completion callback#
Creating profiling context with completion callback:
...
rocprofiler_properties_t properties = {};
properties.callback = completion_callback;
properties.callback_arg = NULL; // no args defined
status = rocprofiler_open(agent, features, 3, &context,
ROCPROFILER_MODE_SINGLEGROUP, properties);
<check status>
...
Definition of completion callback:
void completion_callback(profiler_group_t group, void* arg) {
<tool_dump_data_method(group)>
hsa_status_t status = rocprofiler_close(group.context);
<check status>
}
Option to use context pool#
Code example of context pool usage.
Creating profiling contexts pool:
...
rocprofiler_pool_properties_t properties{};
properties.num_entries = 100;
properties.payload_bytes = sizeof(context_entry_t);
properties.handler = context_handler;
properties.handler_arg = handler_arg;
status = rocprofiler_pool_open(agent, features, 3, &context,
ROCPROFILER_MODE_SINGLEGROUP, properties);
<check status>
...
Fetching a context entry:
rocprofiler_pool_entry_t pool_entry{};
status = rocprofiler_pool_fetch(pool, &pool_entry);
<check status>
// Profiling context entry
rocprofiler_t* context = pool_entry.context;
context_entry_t* entry = reinterpret_cast <context_entry_t*>
(pool_entry.payload);
Standalone sampling usage code example#
The profiling metrics are being read from separate standalone queue other than the application kernels are submitted to.
To enable the sampling mode, the profiling mode in all user queues should be enabled. It can be done by loading ROC-profiler
library to HSA runtime using the environment variable HSA_TOOLS_LIB for all shell sessions.
// Sampling rate
uint32_t sampling_rate = <some rate>;
// Sampling count
uint32_t sampling_count = <some count>;
// HSA status
hsa_status_t status = HSA_STATUS_ERROR;
// HSA agent
hsa_agent_t agent;
// Profiling context
rocprofiler_t* context = NULL;
// Profiling properties
rocprofiler_properties_t properties;
// Getting HSA agent
<query for HSA agent by ‘hsa_iterate_agents()’>
// Profiling feature objects
const unsigned feature_count = 2;
rocprofiler_feature_t feature[feature_count];
// Counters and metrics
feature[0].kind = ROCPROFILER_FEATURE_KIND_METRIC;
feature[0].name = "GPUBusy";
feature[1].kind = ROCPROFILER_FEATURE_KIND_METRIC;
feature[1].name = "SQ_WAVES";
// Creating profiling context with standalone queue
properties = {};
properties.queue_depth = 128;
status = rocprofiler_open(agent, feature, feature_count, &context,
ROCPROFILER_MODE_STANDALONE| ROCPROFILER_MODE_CREATEQUEUE|
ROCPROFILER_MODE_SINGLEGROUP, &properties);
<check status>
// Start counters and sample them in the loop with the sampling rate
status = rocprofiler_start(context, 0);
<check status>
for (unsigned ind = 0; ind < sampling_count; ++ind) {
sleep(sampling_rate);
status = rocprofiler_read(context, 0);
<check status>
status = rocprofiler_get_data(context, 0);
<check status>
status = rocprofiler_get_metrics(context);
<check status>
print_results(feature, feature_count);
}
// Stop counters
status = rocprofiler_stop(context, group_n);
<check status>
// Finishing cleanup
// Deleting profiling context will delete all allocated resources
status = rocprofiler_close(context);
<check status>
Printing out profiling results#
The following is a code example for printing out the profiling results from profiling features array:
void print_results(rocprofiler_feature_t* feature, uint32_t feature_count) {
for (rocprofiler_feature_t* p = feature; p < feature + feature_count; ++p)
{
std::cout << (p - feature) << ": " << p->name;
switch (p->data.kind) {
case ROCPROFILER_DATA_KIND_INT64:
std::cout << " result_int64 (" << p->data.result_int64 << ")"
<< std::endl;
break;
case ROCPROFILER_DATA_KIND_BYTES: {
std::cout << " result_bytes ptr(" << p->data.result_bytes.ptr <<
") " << " size(" << p->data.result_bytes.size << ")"
<< " instance_count(" << p->data.result_bytes.instance_count
<< ")";
break;
}
default:
std::cout << "bad result kind (" << p->data.kind << ")"
<< std::endl;
<abort>
}
}
}