/* * This file is part of the StarPU Handbook. * Copyright (C) 2009--2011 Universit@'e de Bordeaux 1 * Copyright (C) 2010, 2011, 2012, 2013, 2014 Centre National de la Recherche Scientifique * Copyright (C) 2011, 2012 Institut National de Recherche en Informatique et Automatique * See the file version.doxy for copying conditions. */ /*! \page CheckListWhenPerformanceAreNotThere Check List When Performance Are Not There TODO: improve! Simply encapsulating application kernels into tasks already permits to seamlessly support CPU and GPUs at the same time. To achieve good performance, we give below a list of features which should be checked. \section DataRelatedFeaturesToImprovePerformance Data Related Features That May Improve Performance link to \ref DataManagement link to \ref DataPrefetch \section TaskRelatedFeaturesToImprovePerformance Task Related Features That May Improve Performance link to \ref TaskGranularity link to \ref TaskSubmission link to \ref TaskPriorities \section SchedulingRelatedFeaturesToImprovePerformance Scheduling Related Features That May Improve Performance link to \ref TaskSchedulingPolicy link to \ref TaskDistributionVsDataTransfer link to \ref Power-basedScheduling link to \ref StaticScheduling \section CUDA-specificOptimizations CUDA-specific Optimizations Due to CUDA limitations, StarPU will have a hard time overlapping its own communications and the codelet computations if the application does not use a dedicated CUDA stream for its computations instead of the default stream, which synchronizes all operations of the GPU. StarPU provides one by the use of starpu_cuda_get_local_stream() which can be used by all CUDA codelet operations to avoid this issue. For instance: \code{.c} func <<>> (foo, bar); cudaStreamSynchronize(starpu_cuda_get_local_stream()); \endcode Unfortunately, some CUDA libraries do not have stream variants of kernels. That will lower the potential for overlapping. StarPU already does appropriate calls for the CUBLAS library. If the kernel can be made to only use this local stream or other self-allocated streams, i.e. the whole kernel submission can be made asynchronous, then one should enable asynchronous execution of the kernel. That means setting the STARPU_CUDA_ASYNC flag in cuda_flags[] in the codelet, and dropping the cudaStreamSynchronize() call at the end of the cuda_func function, so that it returns immediately after having queued the kernel to the local stream. That way, StarPU will be able to submit and complete data transfers while kernels are executing, instead of only at each kernel submission. The kernel just has to make sure that StarPU can use the local stream to synchronize with the kernel startup and completion. Using the STARPU_CUDA_ASYNC flag also permits to enable concurrent kernel execution, on cards which support it (Kepler and later, notably). This is enabled by setting the STARPU_NWORKER_PER_CUDA environment variable to the number of kernels to execute concurrently. This is useful when kernels are small and do not feed the whole GPU with threads to run. \section OpenCL-specificOptimizations OpenCL-specific Optimizations If the kernel can be made to only use the StarPU-provided command queue or other self-allocated queues, i.e. the whole kernel submission can be made asynchronous, then one should enable asynchronous execution of the kernel. This means setting the corresponding opencl_flags[] flag in the codelet and dropping the clFinish() and starpu_opencl_collect_stats() calls at the end of the kernel, so that it returns immediately after having queued the kernel to the provided queue. That way, StarPU will be able to submit and complete data transfers while kernels are executing, instead of only at each kernel submission. The kernel just has to make sure that StarPU can use the command queue it has provided to synchronize with the kernel startup and completion. \section DetectionStuckConditions Detection Stuck Conditions It may happen that for some reason, StarPU does not make progress for a long period of time. Reason are sometimes due to contention inside StarPU, but sometimes this is due to external reasons, such as stuck MPI driver, or CUDA driver, etc. export STARPU_WATCHDOG_TIMEOUT=10000 allows to make StarPU print an error message whenever StarPU does not terminate any task for 10ms. In addition to that, export STARPU_WATCHDOG_CRASH=1 raises SIGABRT in that condition, thus allowing to catch the situation in gdb. It can also be useful to type "handle SIGABRT nopass" in gdb to be able to let the process continue, after inspecting the state of the process. \section HowToLimitMemoryPerNode How to limit memory per node TODO Talk about \ref STARPU_LIMIT_CUDA_devid_MEM, \ref STARPU_LIMIT_CUDA_MEM, \ref STARPU_LIMIT_OPENCL_devid_MEM, \ref STARPU_LIMIT_OPENCL_MEM and \ref STARPU_LIMIT_CPU_MEM starpu_memory_get_total() starpu_memory_get_available() \section PerformanceModelCalibration Performance Model Calibration Most schedulers are based on an estimation of codelet duration on each kind of processing unit. For this to be possible, the application programmer needs to configure a performance model for the codelets of the application (see \ref PerformanceModelExample for instance). History-based performance models use on-line calibration. StarPU will automatically calibrate codelets which have never been calibrated yet, and save the result in $STARPU_HOME/.starpu/sampling/codelets. The models are indexed by machine name. To share the models between machines (e.g. for a homogeneous cluster), use export STARPU_HOSTNAME=some_global_name. To force continuing calibration, use export STARPU_CALIBRATE=1 . This may be necessary if your application has not-so-stable performance. StarPU will force calibration (and thus ignore the current result) until 10 (_STARPU_CALIBRATION_MINIMUM) measurements have been made on each architecture, to avoid badly scheduling tasks just because the first measurements were not so good. Details on the current performance model status can be obtained from the command starpu_perfmodel_display: the -l option lists the available performance models, and the -s option permits to choose the performance model to be displayed. The result looks like: \verbatim $ starpu_perfmodel_display -s starpu_slu_lu_model_11 performance model for cpu_impl_0 # hash size flops mean dev n 914f3bef 1048576 0.000000e+00 2.503577e+04 1.982465e+02 8 3e921964 65536 0.000000e+00 5.527003e+02 1.848114e+01 7 e5a07e31 4096 0.000000e+00 1.717457e+01 5.190038e+00 14 ... \endverbatim Which shows that for the LU 11 kernel with a 1MiB matrix, the average execution time on CPUs was about 25ms, with a 0.2ms standard deviation, over 8 samples. It is a good idea to check this before doing actual performance measurements. A graph can be drawn by using the tool starpu_perfmodel_plot: \verbatim $ starpu_perfmodel_plot -s starpu_slu_lu_model_11 4096 16384 65536 262144 1048576 4194304 $ gnuplot starpu_starpu_slu_lu_model_11.gp $ gv starpu_starpu_slu_lu_model_11.eps \endverbatim \image html starpu_starpu_slu_lu_model_11.png \image latex starpu_starpu_slu_lu_model_11.eps "" width=\textwidth If a kernel source code was modified (e.g. performance improvement), the calibration information is stale and should be dropped, to re-calibrate from start. This can be done by using export STARPU_CALIBRATE=2. Note: history-based performance models get calibrated only if a performance-model-based scheduler is chosen. The history-based performance models can also be explicitly filled by the application without execution, if e.g. the application already has a series of measurements. This can be done by using starpu_perfmodel_update_history(), for instance: \code{.c} static struct starpu_perfmodel perf_model = { .type = STARPU_HISTORY_BASED, .symbol = "my_perfmodel", }; struct starpu_codelet cl = { .cuda_funcs = { cuda_func1, cuda_func2, NULL }, .nbuffers = 1, .modes = {STARPU_W}, .model = &perf_model }; void feed(void) { struct my_measure *measure; struct starpu_task task; starpu_task_init(&task); task.cl = &cl; for (measure = &measures[0]; measure < measures[last]; measure++) { starpu_data_handle_t handle; starpu_vector_data_register(&handle, -1, 0, measure->size, sizeof(float)); task.handles[0] = handle; starpu_perfmodel_update_history(&perf_model, &task, STARPU_CUDA_DEFAULT + measure->cudadev, 0, measure->implementation, measure->time); starpu_task_clean(&task); starpu_data_unregister(handle); } } \endcode Measurement has to be provided in milliseconds for the completion time models, and in Joules for the energy consumption models. \section Profiling Profiling A quick view of how many tasks each worker has executed can be obtained by setting export STARPU_WORKER_STATS=1 This is a convenient way to check that execution did happen on accelerators, without penalizing performance with the profiling overhead. A quick view of how much data transfers have been issued can be obtained by setting export STARPU_BUS_STATS=1 . More detailed profiling information can be enabled by using export STARPU_PROFILING=1 or by calling starpu_profiling_status_set() from the source code. Statistics on the execution can then be obtained by using export STARPU_BUS_STATS=1 and export STARPU_WORKER_STATS=1 . More details on performance feedback are provided by the next chapter. */