otf2cli_perf_patterns ( obj ) ¶ĭictionary of default perf_patterns for the tool _scorep_openacc. If SCOREP_METRIC_RUSAGE is defined then return the metric name. Executia de kernels este sincrona cand se ruleaza cu un profiler (Nsight, Visual Profiler). If SCOREP_METRIC_RUSAGE is defined then return the otf-profilerįlags so that it will not segfault. Pentru a face debug unor scenarii de executie asincrona se poate dezactiva complet executia asincrona setand variabila de mediu CUDALAUNCHBLOCKING la 1. Fortran CUDA is possible through the use of PGI-fortran. The CUDA platform is designed to work with programming languages such as C, C++. It allows you to program a CUDA-enabled graphics processing unit (GPU) for general-purpose processing.
Otf_profiler ( ) ¶ Sanity checks ¶ _scorep_openacc. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. SphExaNativeCheck ( * args : Any, ** kwargs : Any ) ¶
User/system time, maximum resident set size, and number of page faults: Rumetric – Record Linux Resource Usage Counters to provide informationĪbout consumed resources and operating system events such as Obtained via sampling/unwinding cannot be filtered) => cycles is set to
#Nvprof cudalaunch code#
This class runs the test code with Score-P (MPI+OpenACC):Ĥ parameters can be set for simulation: ParametersĬycles – Compiler-instrumented code is required for OpenACC (regions > nvprof - version nvprof : NVIDIA ( R ) Cuda command line profiler Copyright ( c ) 2012 - 2019 NVIDIA Corporation Release version 10.2.89 ( 21 ) ^^^^^^^ returns : True or False scorep_openacc.py ¶ class _openacc. Reports Memory Operation (KiB) measured by the nsys_perf_patterns ( obj ) ¶ĭictionary of default nsys_perf_patterns for the tool _nvidia. SphExaNsysCudaCheck ( * args : Any, ** kwargs : Any ) ¶
#Nvprof cudalaunch Patch#
Square patch test is set with a dictionary depending on mpitask,īut cubesize could also be on the list of parameters,Ĭudatoolkit/10.2.89 has nsys/2019.5.2.16-b54ef97 To represent textures, GPGPU-Sim uses a system of texture names, texture references (texref), cudaArrays, textureInfos, and textureReferenceAttrs. The CUDA Runtime API equivalent is cudaLaunch, which was already supported by GPGPU-Sim. Mpitask – number of mpi tasks the size of the cube in the 3D The most closely related tool to GPGPU-Sim is NVProf. This class runs the test code with Nvidia nsys systems (2 mpi tasks min)Īvailable analysis types are: nsys profile -helpĢ parameters can be set for simulation: Parameters The efficacy of software-level optimizations can vary significantly when used in different deployment configurations. SphExaNsysCudaCheck ( * args : Any, ** kwargs : Any ) ¶ Modern deep neural network (DNN) training jobs use complex and heterogeneous software/hardware stacks. GPU Reference Guide ¶ Regression tests ¶ nsys_cuda.py ¶ class _cuda.
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