WIP: Add TACUDA package #18
@ -1,4 +1,4 @@
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{ lib, config, ... }:
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{ lib, config, pkgs, ... }:
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{
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# Configure Nvidia driver to use with CUDA
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hardware.nvidia.package = config.boot.kernelPackages.nvidiaPackages.production;
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@ -15,4 +15,6 @@
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programs.nix-required-mounts.allowedPatterns.nvidia-gpu.paths = [
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config.systemd.tmpfiles.settings.graphics-driver."/run/opengl-driver"."L+".argument
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];
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environment.systemPackages = [ pkgs.cudainfo ];
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}
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12
pkgs/cudainfo/Makefile
Normal file
12
pkgs/cudainfo/Makefile
Normal file
@ -0,0 +1,12 @@
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HOSTCXX ?= g++
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NVCC := nvcc -ccbin $(HOSTCXX)
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CXXFLAGS := -m64
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# Target rules
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all: cudainfo
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cudainfo: cudainfo.cpp
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$(NVCC) $(CXXFLAGS) -o $@ $<
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clean:
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rm -f cudainfo cudainfo.o
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600
pkgs/cudainfo/cudainfo.cpp
Normal file
600
pkgs/cudainfo/cudainfo.cpp
Normal file
@ -0,0 +1,600 @@
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/*
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* Copyright 1993-2015 NVIDIA Corporation. All rights reserved.
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*
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* Please refer to the NVIDIA end user license agreement (EULA) associated
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* with this source code for terms and conditions that govern your use of
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* this software. Any use, reproduction, disclosure, or distribution of
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* this software and related documentation outside the terms of the EULA
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* is strictly prohibited.
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*
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*/
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/* This sample queries the properties of the CUDA devices present in the system via CUDA Runtime API. */
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// Shared Utilities (QA Testing)
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// std::system includes
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#include <memory>
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#include <iostream>
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#include <cuda_runtime.h>
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// This will output the proper CUDA error strings in the event that a CUDA host call returns an error
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#define checkCudaErrors(val) check ( (val), #val, __FILE__, __LINE__ )
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// CUDA Runtime error messages
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#ifdef __DRIVER_TYPES_H__
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static const char *_cudaGetErrorEnum(cudaError_t error)
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{
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switch (error)
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{
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case cudaSuccess:
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return "cudaSuccess";
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case cudaErrorMissingConfiguration:
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return "cudaErrorMissingConfiguration";
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case cudaErrorMemoryAllocation:
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return "cudaErrorMemoryAllocation";
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case cudaErrorInitializationError:
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return "cudaErrorInitializationError";
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case cudaErrorLaunchFailure:
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return "cudaErrorLaunchFailure";
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case cudaErrorPriorLaunchFailure:
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return "cudaErrorPriorLaunchFailure";
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case cudaErrorLaunchTimeout:
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return "cudaErrorLaunchTimeout";
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case cudaErrorLaunchOutOfResources:
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return "cudaErrorLaunchOutOfResources";
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case cudaErrorInvalidDeviceFunction:
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return "cudaErrorInvalidDeviceFunction";
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case cudaErrorInvalidConfiguration:
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return "cudaErrorInvalidConfiguration";
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case cudaErrorInvalidDevice:
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return "cudaErrorInvalidDevice";
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case cudaErrorInvalidValue:
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return "cudaErrorInvalidValue";
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case cudaErrorInvalidPitchValue:
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return "cudaErrorInvalidPitchValue";
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case cudaErrorInvalidSymbol:
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return "cudaErrorInvalidSymbol";
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case cudaErrorMapBufferObjectFailed:
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return "cudaErrorMapBufferObjectFailed";
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case cudaErrorUnmapBufferObjectFailed:
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return "cudaErrorUnmapBufferObjectFailed";
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case cudaErrorInvalidHostPointer:
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return "cudaErrorInvalidHostPointer";
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case cudaErrorInvalidDevicePointer:
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return "cudaErrorInvalidDevicePointer";
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case cudaErrorInvalidTexture:
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return "cudaErrorInvalidTexture";
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case cudaErrorInvalidTextureBinding:
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return "cudaErrorInvalidTextureBinding";
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case cudaErrorInvalidChannelDescriptor:
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return "cudaErrorInvalidChannelDescriptor";
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case cudaErrorInvalidMemcpyDirection:
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return "cudaErrorInvalidMemcpyDirection";
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case cudaErrorAddressOfConstant:
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return "cudaErrorAddressOfConstant";
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case cudaErrorTextureFetchFailed:
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return "cudaErrorTextureFetchFailed";
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case cudaErrorTextureNotBound:
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return "cudaErrorTextureNotBound";
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case cudaErrorSynchronizationError:
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return "cudaErrorSynchronizationError";
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case cudaErrorInvalidFilterSetting:
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return "cudaErrorInvalidFilterSetting";
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case cudaErrorInvalidNormSetting:
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return "cudaErrorInvalidNormSetting";
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case cudaErrorMixedDeviceExecution:
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return "cudaErrorMixedDeviceExecution";
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case cudaErrorCudartUnloading:
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return "cudaErrorCudartUnloading";
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case cudaErrorUnknown:
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return "cudaErrorUnknown";
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case cudaErrorNotYetImplemented:
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return "cudaErrorNotYetImplemented";
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case cudaErrorMemoryValueTooLarge:
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return "cudaErrorMemoryValueTooLarge";
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case cudaErrorInvalidResourceHandle:
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return "cudaErrorInvalidResourceHandle";
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case cudaErrorNotReady:
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return "cudaErrorNotReady";
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case cudaErrorInsufficientDriver:
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return "cudaErrorInsufficientDriver";
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case cudaErrorSetOnActiveProcess:
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return "cudaErrorSetOnActiveProcess";
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case cudaErrorInvalidSurface:
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return "cudaErrorInvalidSurface";
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case cudaErrorNoDevice:
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return "cudaErrorNoDevice";
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case cudaErrorECCUncorrectable:
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return "cudaErrorECCUncorrectable";
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case cudaErrorSharedObjectSymbolNotFound:
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return "cudaErrorSharedObjectSymbolNotFound";
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case cudaErrorSharedObjectInitFailed:
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return "cudaErrorSharedObjectInitFailed";
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case cudaErrorUnsupportedLimit:
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return "cudaErrorUnsupportedLimit";
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case cudaErrorDuplicateVariableName:
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return "cudaErrorDuplicateVariableName";
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case cudaErrorDuplicateTextureName:
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return "cudaErrorDuplicateTextureName";
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case cudaErrorDuplicateSurfaceName:
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return "cudaErrorDuplicateSurfaceName";
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case cudaErrorDevicesUnavailable:
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return "cudaErrorDevicesUnavailable";
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case cudaErrorInvalidKernelImage:
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return "cudaErrorInvalidKernelImage";
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case cudaErrorNoKernelImageForDevice:
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return "cudaErrorNoKernelImageForDevice";
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case cudaErrorIncompatibleDriverContext:
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return "cudaErrorIncompatibleDriverContext";
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case cudaErrorPeerAccessAlreadyEnabled:
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return "cudaErrorPeerAccessAlreadyEnabled";
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case cudaErrorPeerAccessNotEnabled:
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return "cudaErrorPeerAccessNotEnabled";
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case cudaErrorDeviceAlreadyInUse:
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return "cudaErrorDeviceAlreadyInUse";
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case cudaErrorProfilerDisabled:
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return "cudaErrorProfilerDisabled";
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case cudaErrorProfilerNotInitialized:
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return "cudaErrorProfilerNotInitialized";
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case cudaErrorProfilerAlreadyStarted:
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return "cudaErrorProfilerAlreadyStarted";
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case cudaErrorProfilerAlreadyStopped:
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return "cudaErrorProfilerAlreadyStopped";
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/* Since CUDA 4.0*/
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case cudaErrorAssert:
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return "cudaErrorAssert";
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case cudaErrorTooManyPeers:
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return "cudaErrorTooManyPeers";
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case cudaErrorHostMemoryAlreadyRegistered:
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return "cudaErrorHostMemoryAlreadyRegistered";
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case cudaErrorHostMemoryNotRegistered:
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return "cudaErrorHostMemoryNotRegistered";
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/* Since CUDA 5.0 */
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case cudaErrorOperatingSystem:
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return "cudaErrorOperatingSystem";
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case cudaErrorPeerAccessUnsupported:
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return "cudaErrorPeerAccessUnsupported";
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case cudaErrorLaunchMaxDepthExceeded:
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return "cudaErrorLaunchMaxDepthExceeded";
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case cudaErrorLaunchFileScopedTex:
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return "cudaErrorLaunchFileScopedTex";
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case cudaErrorLaunchFileScopedSurf:
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return "cudaErrorLaunchFileScopedSurf";
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case cudaErrorSyncDepthExceeded:
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return "cudaErrorSyncDepthExceeded";
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case cudaErrorLaunchPendingCountExceeded:
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return "cudaErrorLaunchPendingCountExceeded";
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case cudaErrorNotPermitted:
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return "cudaErrorNotPermitted";
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case cudaErrorNotSupported:
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return "cudaErrorNotSupported";
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/* Since CUDA 6.0 */
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case cudaErrorHardwareStackError:
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return "cudaErrorHardwareStackError";
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case cudaErrorIllegalInstruction:
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return "cudaErrorIllegalInstruction";
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case cudaErrorMisalignedAddress:
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return "cudaErrorMisalignedAddress";
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case cudaErrorInvalidAddressSpace:
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return "cudaErrorInvalidAddressSpace";
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case cudaErrorInvalidPc:
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return "cudaErrorInvalidPc";
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case cudaErrorIllegalAddress:
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return "cudaErrorIllegalAddress";
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/* Since CUDA 6.5*/
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case cudaErrorInvalidPtx:
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return "cudaErrorInvalidPtx";
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case cudaErrorInvalidGraphicsContext:
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return "cudaErrorInvalidGraphicsContext";
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case cudaErrorStartupFailure:
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return "cudaErrorStartupFailure";
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case cudaErrorApiFailureBase:
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return "cudaErrorApiFailureBase";
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}
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return "<unknown>";
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}
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#endif
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template< typename T >
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void check(T result, char const *const func, const char *const file, int const line)
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{
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if (result)
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{
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fprintf(stderr, "CUDA error at %s:%d code=%d(%s) \"%s\" \n",
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file, line, static_cast<unsigned int>(result), _cudaGetErrorEnum(result), func);
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cudaDeviceReset();
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// Make sure we call CUDA Device Reset before exiting
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exit(EXIT_FAILURE);
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}
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}
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int *pArgc = NULL;
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char **pArgv = NULL;
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#if CUDART_VERSION < 5000
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// CUDA-C includes
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#include <cuda.h>
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// This function wraps the CUDA Driver API into a template function
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template <class T>
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inline void getCudaAttribute(T *attribute, CUdevice_attribute device_attribute, int device)
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{
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CUresult error = cuDeviceGetAttribute(attribute, device_attribute, device);
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if (CUDA_SUCCESS != error) {
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fprintf(stderr, "cuSafeCallNoSync() Driver API error = %04d from file <%s>, line %i.\n",
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error, __FILE__, __LINE__);
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// cudaDeviceReset causes the driver to clean up all state. While
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// not mandatory in normal operation, it is good practice. It is also
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// needed to ensure correct operation when the application is being
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// profiled. Calling cudaDeviceReset causes all profile data to be
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// flushed before the application exits
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cudaDeviceReset();
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exit(EXIT_FAILURE);
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}
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}
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#endif /* CUDART_VERSION < 5000 */
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// Beginning of GPU Architecture definitions
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inline int ConvertSMVer2Cores(int major, int minor)
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{
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// Defines for GPU Architecture types (using the SM version to determine the # of cores per SM
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typedef struct {
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int SM; // 0xMm (hexidecimal notation), M = SM Major version, and m = SM minor version
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int Cores;
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} sSMtoCores;
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sSMtoCores nGpuArchCoresPerSM[] = {
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{ 0x20, 32 }, // Fermi Generation (SM 2.0) GF100 class
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{ 0x21, 48 }, // Fermi Generation (SM 2.1) GF10x class
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{ 0x30, 192}, // Kepler Generation (SM 3.0) GK10x class
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{ 0x32, 192}, // Kepler Generation (SM 3.2) GK10x class
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{ 0x35, 192}, // Kepler Generation (SM 3.5) GK11x class
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{ 0x37, 192}, // Kepler Generation (SM 3.7) GK21x class
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{ 0x50, 128}, // Maxwell Generation (SM 5.0) GM10x class
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{ 0x52, 128}, // Maxwell Generation (SM 5.2) GM20x class
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{ -1, -1 }
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};
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int index = 0;
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while (nGpuArchCoresPerSM[index].SM != -1) {
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if (nGpuArchCoresPerSM[index].SM == ((major << 4) + minor)) {
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return nGpuArchCoresPerSM[index].Cores;
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}
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index++;
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}
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// If we don't find the values, we default use the previous one to run properly
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printf("MapSMtoCores for SM %d.%d is undefined. Default to use %d Cores/SM\n", major, minor, nGpuArchCoresPerSM[index-1].Cores);
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return nGpuArchCoresPerSM[index-1].Cores;
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}
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////////////////////////////////////////////////////////////////////////////////
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// Program main
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////////////////////////////////////////////////////////////////////////////////
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int
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main(int argc, char **argv)
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{
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pArgc = &argc;
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pArgv = argv;
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printf("%s Starting...\n\n", argv[0]);
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printf(" CUDA Device Query (Runtime API) version (CUDART static linking)\n\n");
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int deviceCount = 0;
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cudaError_t error_id = cudaGetDeviceCount(&deviceCount);
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if (error_id != cudaSuccess) {
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printf("cudaGetDeviceCount failed: %s (%d)\n",
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cudaGetErrorString(error_id), (int) error_id);
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printf("Result = FAIL\n");
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exit(EXIT_FAILURE);
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}
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// This function call returns 0 if there are no CUDA capable devices.
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if (deviceCount == 0)
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printf("There are no available device(s) that support CUDA\n");
|
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else
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printf("Detected %d CUDA Capable device(s)\n", deviceCount);
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|
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int dev, driverVersion = 0, runtimeVersion = 0;
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|
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for (dev = 0; dev < deviceCount; ++dev) {
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cudaSetDevice(dev);
|
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cudaDeviceProp deviceProp;
|
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cudaGetDeviceProperties(&deviceProp, dev);
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|
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printf("\nDevice %d: \"%s\"\n", dev, deviceProp.name);
|
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|
||||
// Console log
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||||
cudaDriverGetVersion(&driverVersion);
|
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cudaRuntimeGetVersion(&runtimeVersion);
|
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printf(" CUDA Driver Version / Runtime Version %d.%d / %d.%d\n", driverVersion/1000, (driverVersion%100)/10, runtimeVersion/1000, (runtimeVersion%100)/10);
|
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printf(" CUDA Capability Major/Minor version number: %d.%d\n", deviceProp.major, deviceProp.minor);
|
||||
|
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printf(" Total amount of global memory: %.0f MBytes (%llu bytes)\n",
|
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(float)deviceProp.totalGlobalMem/1048576.0f, (unsigned long long) deviceProp.totalGlobalMem);
|
||||
|
||||
printf(" (%2d) Multiprocessors, (%3d) CUDA Cores/MP: %d CUDA Cores\n",
|
||||
deviceProp.multiProcessorCount,
|
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ConvertSMVer2Cores(deviceProp.major, deviceProp.minor),
|
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ConvertSMVer2Cores(deviceProp.major, deviceProp.minor) * deviceProp.multiProcessorCount);
|
||||
printf(" GPU Max Clock rate: %.0f MHz (%0.2f GHz)\n", deviceProp.clockRate * 1e-3f, deviceProp.clockRate * 1e-6f);
|
||||
|
||||
|
||||
#if CUDART_VERSION >= 5000
|
||||
// This is supported in CUDA 5.0 (runtime API device properties)
|
||||
printf(" Memory Clock rate: %.0f Mhz\n", deviceProp.memoryClockRate * 1e-3f);
|
||||
printf(" Memory Bus Width: %d-bit\n", deviceProp.memoryBusWidth);
|
||||
|
||||
if (deviceProp.l2CacheSize) {
|
||||
printf(" L2 Cache Size: %d bytes\n", deviceProp.l2CacheSize);
|
||||
}
|
||||
|
||||
#else
|
||||
// This only available in CUDA 4.0-4.2 (but these were only exposed in the CUDA Driver API)
|
||||
int memoryClock;
|
||||
getCudaAttribute<int>(&memoryClock, CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE, dev);
|
||||
printf(" Memory Clock rate: %.0f Mhz\n", memoryClock * 1e-3f);
|
||||
int memBusWidth;
|
||||
getCudaAttribute<int>(&memBusWidth, CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH, dev);
|
||||
printf(" Memory Bus Width: %d-bit\n", memBusWidth);
|
||||
int L2CacheSize;
|
||||
getCudaAttribute<int>(&L2CacheSize, CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE, dev);
|
||||
|
||||
if (L2CacheSize) {
|
||||
printf(" L2 Cache Size: %d bytes\n", L2CacheSize);
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
printf(" Maximum Texture Dimension Size (x,y,z) 1D=(%d), 2D=(%d, %d), 3D=(%d, %d, %d)\n",
|
||||
deviceProp.maxTexture1D , deviceProp.maxTexture2D[0], deviceProp.maxTexture2D[1],
|
||||
deviceProp.maxTexture3D[0], deviceProp.maxTexture3D[1], deviceProp.maxTexture3D[2]);
|
||||
printf(" Maximum Layered 1D Texture Size, (num) layers 1D=(%d), %d layers\n",
|
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deviceProp.maxTexture1DLayered[0], deviceProp.maxTexture1DLayered[1]);
|
||||
printf(" Maximum Layered 2D Texture Size, (num) layers 2D=(%d, %d), %d layers\n",
|
||||
deviceProp.maxTexture2DLayered[0], deviceProp.maxTexture2DLayered[1], deviceProp.maxTexture2DLayered[2]);
|
||||
|
||||
|
||||
printf(" Total amount of constant memory: %lu bytes\n", deviceProp.totalConstMem);
|
||||
printf(" Total amount of shared memory per block: %lu bytes\n", deviceProp.sharedMemPerBlock);
|
||||
printf(" Total number of registers available per block: %d\n", deviceProp.regsPerBlock);
|
||||
printf(" Warp size: %d\n", deviceProp.warpSize);
|
||||
printf(" Maximum number of threads per multiprocessor: %d\n", deviceProp.maxThreadsPerMultiProcessor);
|
||||
printf(" Maximum number of threads per block: %d\n", deviceProp.maxThreadsPerBlock);
|
||||
printf(" Max dimension size of a thread block (x,y,z): (%d, %d, %d)\n",
|
||||
deviceProp.maxThreadsDim[0],
|
||||
deviceProp.maxThreadsDim[1],
|
||||
deviceProp.maxThreadsDim[2]);
|
||||
printf(" Max dimension size of a grid size (x,y,z): (%d, %d, %d)\n",
|
||||
deviceProp.maxGridSize[0],
|
||||
deviceProp.maxGridSize[1],
|
||||
deviceProp.maxGridSize[2]);
|
||||
printf(" Maximum memory pitch: %lu bytes\n", deviceProp.memPitch);
|
||||
printf(" Texture alignment: %lu bytes\n", deviceProp.textureAlignment);
|
||||
printf(" Concurrent copy and kernel execution: %s with %d copy engine(s)\n", (deviceProp.deviceOverlap ? "Yes" : "No"), deviceProp.asyncEngineCount);
|
||||
printf(" Run time limit on kernels: %s\n", deviceProp.kernelExecTimeoutEnabled ? "Yes" : "No");
|
||||
printf(" Integrated GPU sharing Host Memory: %s\n", deviceProp.integrated ? "Yes" : "No");
|
||||
printf(" Support host page-locked memory mapping: %s\n", deviceProp.canMapHostMemory ? "Yes" : "No");
|
||||
printf(" Alignment requirement for Surfaces: %s\n", deviceProp.surfaceAlignment ? "Yes" : "No");
|
||||
printf(" Device has ECC support: %s\n", deviceProp.ECCEnabled ? "Enabled" : "Disabled");
|
||||
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
|
||||
printf(" CUDA Device Driver Mode (TCC or WDDM): %s\n", deviceProp.tccDriver ? "TCC (Tesla Compute Cluster Driver)" : "WDDM (Windows Display Driver Model)");
|
||||
#endif
|
||||
printf(" Device supports Unified Addressing (UVA): %s\n", deviceProp.unifiedAddressing ? "Yes" : "No");
|
||||
printf(" Device PCI Domain ID / Bus ID / location ID: %d / %d / %d\n", deviceProp.pciDomainID, deviceProp.pciBusID, deviceProp.pciDeviceID);
|
||||
|
||||
const char *sComputeMode[] = {
|
||||
"Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)",
|
||||
"Exclusive (only one host thread in one process is able to use ::cudaSetDevice() with this device)",
|
||||
"Prohibited (no host thread can use ::cudaSetDevice() with this device)",
|
||||
"Exclusive Process (many threads in one process is able to use ::cudaSetDevice() with this device)",
|
||||
"Unknown",
|
||||
NULL
|
||||
};
|
||||
printf(" Compute Mode:\n");
|
||||
printf(" < %s >\n", sComputeMode[deviceProp.computeMode]);
|
||||
}
|
||||
|
||||
// If there are 2 or more GPUs, query to determine whether RDMA is supported
|
||||
if (deviceCount >= 2)
|
||||
{
|
||||
cudaDeviceProp prop[64];
|
||||
int gpuid[64]; // we want to find the first two GPU's that can support P2P
|
||||
int gpu_p2p_count = 0;
|
||||
|
||||
for (int i=0; i < deviceCount; i++)
|
||||
{
|
||||
checkCudaErrors(cudaGetDeviceProperties(&prop[i], i));
|
||||
|
||||
// Only boards based on Fermi or later can support P2P
|
||||
if ((prop[i].major >= 2)
|
||||
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
|
||||
// on Windows (64-bit), the Tesla Compute Cluster driver for windows must be enabled to supprot this
|
||||
&& prop[i].tccDriver
|
||||
#endif
|
||||
)
|
||||
{
|
||||
// This is an array of P2P capable GPUs
|
||||
gpuid[gpu_p2p_count++] = i;
|
||||
}
|
||||
}
|
||||
|
||||
// Show all the combinations of support P2P GPUs
|
||||
int can_access_peer_0_1, can_access_peer_1_0;
|
||||
|
||||
if (gpu_p2p_count >= 2)
|
||||
{
|
||||
for (int i = 0; i < gpu_p2p_count-1; i++)
|
||||
{
|
||||
for (int j = 1; j < gpu_p2p_count; j++)
|
||||
{
|
||||
checkCudaErrors(cudaDeviceCanAccessPeer(&can_access_peer_0_1, gpuid[i], gpuid[j]));
|
||||
printf("> Peer access from %s (GPU%d) -> %s (GPU%d) : %s\n", prop[gpuid[i]].name, gpuid[i],
|
||||
prop[gpuid[j]].name, gpuid[j] ,
|
||||
can_access_peer_0_1 ? "Yes" : "No");
|
||||
}
|
||||
}
|
||||
|
||||
for (int j = 1; j < gpu_p2p_count; j++)
|
||||
{
|
||||
for (int i = 0; i < gpu_p2p_count-1; i++)
|
||||
{
|
||||
checkCudaErrors(cudaDeviceCanAccessPeer(&can_access_peer_1_0, gpuid[j], gpuid[i]));
|
||||
printf("> Peer access from %s (GPU%d) -> %s (GPU%d) : %s\n", prop[gpuid[j]].name, gpuid[j],
|
||||
prop[gpuid[i]].name, gpuid[i] ,
|
||||
can_access_peer_1_0 ? "Yes" : "No");
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// csv masterlog info
|
||||
// *****************************
|
||||
// exe and CUDA driver name
|
||||
printf("\n");
|
||||
std::string sProfileString = "deviceQuery, CUDA Driver = CUDART";
|
||||
char cTemp[128];
|
||||
|
||||
// driver version
|
||||
sProfileString += ", CUDA Driver Version = ";
|
||||
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
|
||||
sprintf_s(cTemp, 10, "%d.%d", driverVersion/1000, (driverVersion%100)/10);
|
||||
#else
|
||||
sprintf(cTemp, "%d.%d", driverVersion/1000, (driverVersion%100)/10);
|
||||
#endif
|
||||
sProfileString += cTemp;
|
||||
|
||||
// Runtime version
|
||||
sProfileString += ", CUDA Runtime Version = ";
|
||||
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
|
||||
sprintf_s(cTemp, 10, "%d.%d", runtimeVersion/1000, (runtimeVersion%100)/10);
|
||||
#else
|
||||
sprintf(cTemp, "%d.%d", runtimeVersion/1000, (runtimeVersion%100)/10);
|
||||
#endif
|
||||
sProfileString += cTemp;
|
||||
|
||||
// Device count
|
||||
sProfileString += ", NumDevs = ";
|
||||
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
|
||||
sprintf_s(cTemp, 10, "%d", deviceCount);
|
||||
#else
|
||||
sprintf(cTemp, "%d", deviceCount);
|
||||
#endif
|
||||
sProfileString += cTemp;
|
||||
|
||||
// Print Out all device Names
|
||||
for (dev = 0; dev < deviceCount; ++dev)
|
||||
{
|
||||
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
|
||||
sprintf_s(cTemp, 13, ", Device%d = ", dev);
|
||||
#else
|
||||
sprintf(cTemp, ", Device%d = ", dev);
|
||||
#endif
|
||||
cudaDeviceProp deviceProp;
|
||||
cudaGetDeviceProperties(&deviceProp, dev);
|
||||
sProfileString += cTemp;
|
||||
sProfileString += deviceProp.name;
|
||||
}
|
||||
|
||||
sProfileString += "\n";
|
||||
printf("%s", sProfileString.c_str());
|
||||
|
||||
printf("Result = PASS\n");
|
||||
|
||||
// finish
|
||||
// cudaDeviceReset causes the driver to clean up all state. While
|
||||
// not mandatory in normal operation, it is good practice. It is also
|
||||
// needed to ensure correct operation when the application is being
|
||||
// profiled. Calling cudaDeviceReset causes all profile data to be
|
||||
// flushed before the application exits
|
||||
cudaDeviceReset();
|
||||
return 0;
|
||||
}
|
||||
43
pkgs/cudainfo/default.nix
Normal file
43
pkgs/cudainfo/default.nix
Normal file
@ -0,0 +1,43 @@
|
||||
{
|
||||
stdenv
|
||||
, cudatoolkit
|
||||
, cudaPackages
|
||||
, autoAddDriverRunpath
|
||||
, strace
|
||||
}:
|
||||
|
||||
stdenv.mkDerivation (finalAttrs: {
|
||||
name = "cudainfo";
|
||||
src = ./.;
|
||||
buildInputs = [
|
||||
cudatoolkit # Required for nvcc
|
||||
cudaPackages.cuda_cudart.static # Required for -lcudart_static
|
||||
autoAddDriverRunpath
|
||||
];
|
||||
installPhase = ''
|
||||
mkdir -p $out/bin
|
||||
cp -a cudainfo $out/bin
|
||||
'';
|
||||
passthru.gpuCheck = stdenv.mkDerivation {
|
||||
name = "cudainfo-test";
|
||||
requiredSystemFeatures = [ "cuda" ];
|
||||
dontBuild = true;
|
||||
nativeCheckInputs = [
|
||||
finalAttrs.finalPackage # The cudainfo package from above
|
||||
strace # When it fails, it will show the trace
|
||||
];
|
||||
dontUnpack = true;
|
||||
doCheck = true;
|
||||
checkPhase = ''
|
||||
if ! cudainfo; then
|
||||
set -x
|
||||
cudainfo=$(command -v cudainfo)
|
||||
ldd $cudainfo
|
||||
readelf -d $cudainfo
|
||||
strace -f $cudainfo
|
||||
set +x
|
||||
fi
|
||||
'';
|
||||
installPhase = "touch $out";
|
||||
};
|
||||
})
|
||||
@ -52,4 +52,5 @@ final: prev:
|
||||
prometheus-slurm-exporter = prev.callPackage ./slurm-exporter.nix { };
|
||||
meteocat-exporter = prev.callPackage ./meteocat-exporter/default.nix { };
|
||||
upc-qaire-exporter = prev.callPackage ./upc-qaire-exporter/default.nix { };
|
||||
cudainfo = prev.callPackage ./cudainfo/default.nix { };
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user