HeterogeneousCore/CUDAServices/plugins/CUDAService.cc - master - githubmirror/cmssw

HeterogeneousCore/CUDAServices/plugins/CUDAService.cc
511 строк22 KB
Andrea Bocci
Rename GPU check macros to uppercase
24 июл 2026, 18:36
24 июл 2026, 18:364ca8016
#include <cstring>
#include <iomanip>
#include <iostream>
#include <limits>
#include <set>
#include <stdexcept>
#include <string>
#include <utility>
#include <vector>

#include <cuda.h>
#include <cuda_runtime.h>
#include <nvml.h>

#include "FWCore/AbstractServices/interface/ResourceInformation.h"
#include "FWCore/MessageLogger/interface/MessageLogger.h"
#include "FWCore/ParameterSet/interface/ConfigurationDescriptions.h"
#include "FWCore/ParameterSet/interface/ParameterSet.h"
#include "FWCore/ParameterSet/interface/ParameterSetDescription.h"
#include "FWCore/ServiceRegistry/interface/Service.h"
#include "FWCore/Utilities/interface/ReusableObjectHolder.h"
#include "HeterogeneousCore/CUDAServices/interface/CUDAInterface.h"
#include "HeterogeneousCore/CUDAUtilities/interface/EventCache.h"
#include "HeterogeneousCore/CUDAUtilities/interface/StreamCache.h"
#include "HeterogeneousCore/CUDAUtilities/interface/cachingAllocators.h"
#include "HeterogeneousCore/CUDAUtilities/interface/cudaCheck.h"
#include "HeterogeneousCore/CUDAUtilities/interface/currentDevice.h"
#include "HeterogeneousCore/CUDAUtilities/interface/device_unique_ptr.h"
#include "HeterogeneousCore/CUDAUtilities/interface/host_unique_ptr.h"
#include "HeterogeneousCore/CUDAUtilities/interface/nvmlCheck.h"

class CUDAService : public CUDAInterface {
public:
  CUDAService(edm::ParameterSet const& config);
  ~CUDAService() override;

  static void fillDescriptions(edm::ConfigurationDescriptions& descriptions);

  bool enabled() const final { return enabled_; }

  int numberOfDevices() const final { return numberOfDevices_; }

  // Return the (major, minor) CUDA compute capability of the given device.
  std::pair<int, int> computeCapability(int device) const final {
    int size = computeCapabilities_.size();
    if (device < 0 or device >= size) {
      throw std::out_of_range("Invalid device index" + std::to_string(device) + ": the valid range is from 0 to " +
                              std::to_string(size - 1));
    }
    return computeCapabilities_[device];
  }

private:
  int numberOfDevices_ = 0;
  std::vector<std::pair<int, int>> computeCapabilities_;
  bool enabled_ = false;
  bool verbose_ = false;
};

void setCudaLimit(cudaLimit limit, const char* name, size_t request) {
  // read the current device
  int device;
  CUDA_CHECK(cudaGetDevice(&device));
  // try to set the requested limit
  auto result = cudaDeviceSetLimit(limit, request);
  if (cudaErrorUnsupportedLimit == result) {
    edm::LogWarning("CUDAService") << "CUDA device " << device << ": unsupported limit \"" << name << "\"";
    return;
  }
  // read back the limit value
  size_t value;
  result = cudaDeviceGetLimit(&value, limit);
  if (cudaSuccess != result) {
    edm::LogWarning("CUDAService") << "CUDA device " << device << ": failed to set limit \"" << name << "\" to "
                                   << request << ", current value is " << value;
  } else if (value != request) {
    edm::LogWarning("CUDAService") << "CUDA device " << device << ": limit \"" << name << "\" set to " << value
                                   << " instead of requested " << request;
  }
}

constexpr unsigned int getCudaCoresPerSM(unsigned int major, unsigned int minor) {
  switch (major * 16 + minor) {
    // Fermi architecture
    case 0x20:  // SM 2.0: GF100 class
      return 32;
    case 0x21:  // SM 2.1: GF10x class
      return 48;

    // Kepler architecture
    case 0x30:  // SM 3.0: GK10x class
    case 0x32:  // SM 3.2: GK10x class
    case 0x35:  // SM 3.5: GK11x class
    case 0x37:  // SM 3.7: GK21x class
      return 192;

    // Maxwell architecture
    case 0x50:  // SM 5.0: GM10x class
    case 0x52:  // SM 5.2: GM20x class
    case 0x53:  // SM 5.3: GM20x class
      return 128;

    // Pascal architecture
    case 0x60:  // SM 6.0: GP100 class
      return 64;
    case 0x61:  // SM 6.1: GP10x class
    case 0x62:  // SM 6.2: GP10x class
      return 128;

    // Volta architecture
    case 0x70:  // SM 7.0: GV100 class
    case 0x72:  // SM 7.2: GV11b class
      return 64;

    // Turing architecture
    case 0x75:  // SM 7.5: TU10x class
      return 64;

    // Ampere architecture
    case 0x80:  // SM 8.0: GA100 class
      return 64;
    case 0x86:  // SM 8.6: GA10x class
    case 0x87:  // SM 8.7: ?
      return 128;

    // Ada Lovelace architectures
    case 0x89:  // SM 8.9: AD10x class
      return 128;

    // Hopper architecture
    case 0x90:  // SM 9.0: GH100 class
      return 128;

    // Blackwell architecture
    case 0xa0:  // SM 10.0: GB100 class
    case 0xa1:  // SM 10.1: GB102 class
      return 128;

    // Blackwell 2.0 architecture
    case 0xc0:  // SM 12.0: GB20x class
      return 128;

    // unknown architecture, return a default value
    default:
      return 128;
  }
}

std::string decodeVersion(int version) {
  return std::to_string(version / 1000) + '.' + std::to_string(version % 1000 / 10);
}

namespace {
  template <template <typename> typename UniquePtr, typename Allocate>
  void preallocate(Allocate allocate, const std::vector<unsigned int>& bufferSizes) {
    if (bufferSizes.empty())
      return;

    auto streamPtr = cms::cuda::getStreamCache().get();

    std::vector<UniquePtr<char[]>> buffers;
    buffers.reserve(bufferSizes.size());
    for (auto size : bufferSizes) {
      buffers.push_back(allocate(size, streamPtr.get()));
    }
  }

  void devicePreallocate(int numberOfDevices, const std::vector<unsigned int>& bufferSizes) {
    int device;
    CUDA_CHECK(cudaGetDevice(&device));
    for (int i = 0; i < numberOfDevices; ++i) {
      CUDA_CHECK(cudaSetDevice(i));
      preallocate<cms::cuda::device::unique_ptr>(
          [&](size_t size, cudaStream_t stream) { return cms::cuda::make_device_unique<char[]>(size, stream); },
          bufferSizes);
    }
    CUDA_CHECK(cudaSetDevice(device));
  }

  void hostPreallocate(const std::vector<unsigned int>& bufferSizes) {
    preallocate<cms::cuda::host::unique_ptr>(
        [&](size_t size, cudaStream_t stream) { return cms::cuda::make_host_unique<char[]>(size, stream); },
        bufferSizes);
  }
}  // namespace

/// Constructor
CUDAService::CUDAService(edm::ParameterSet const& config) : verbose_(config.getUntrackedParameter<bool>("verbose")) {
  if (not config.getUntrackedParameter<bool>("enabled")) {
    edm::LogInfo("CUDAService") << "CUDAService disabled by configuration";
    return;
  }

  auto status = cudaGetDeviceCount(&numberOfDevices_);
  if (cudaSuccess != status) {
    edm::LogWarning("CUDAService") << "Failed to initialize the CUDA runtime.\n"
                                   << "Disabling the CUDAService.";
    return;
  }
  computeCapabilities_.reserve(numberOfDevices_);

  // NVIDIA system driver version, e.g. 470.57.02
  // The NVML interface is not available on gaming GPUs. Just report "unknown" in that case.
  char systemDriverVersion[NVML_SYSTEM_DRIVER_VERSION_BUFFER_SIZE];
  nvmlReturn_t result = nvmlInitWithFlags(NVML_INIT_FLAG_NO_GPUS | NVML_INIT_FLAG_NO_ATTACH);
  if (NVML_SUCCESS != result) {
    edm::LogWarning("CUDAService") << "NVML library not available, cannot query NVIDIA driver version:\n"
                                   << nvmlErrorString(result) << "\nReporting NVIDIA driver version as \"unknown\".";
    std::strncpy(systemDriverVersion, "unknown", sizeof(systemDriverVersion) - 1);
    systemDriverVersion[sizeof(systemDriverVersion) - 1] = '\0';
  } else {
    NVML_CHECK(nvmlSystemGetDriverVersion(systemDriverVersion, sizeof(systemDriverVersion)));
    NVML_CHECK(nvmlShutdown());
  }

  // CUDA driver version, e.g. 11.4
  // the full version, like 11.4.1 or 11.4.100, is not reported
  int driverVersion = 0;
  CUDA_CHECK(cudaDriverGetVersion(&driverVersion));

  // CUDA runtime version, e.g. 11.4
  // the full version, like 11.4.1 or 11.4.108, is not reported
  int runtimeVersion = 0;
  CUDA_CHECK(cudaRuntimeGetVersion(&runtimeVersion));

  edm::LogInfo log("CUDAService");
  if (verbose_) {
    log << "NVIDIA driver:    " << systemDriverVersion << '\n';
    log << "CUDA driver API:  " << decodeVersion(driverVersion) << " (compiled with " << decodeVersion(CUDA_VERSION)
        << ")\n";
    log << "CUDA runtime API: " << decodeVersion(runtimeVersion) << " (compiled with " << decodeVersion(CUDART_VERSION)
        << ")\n";
    log << "CUDA runtime successfully initialised, found " << numberOfDevices_ << " compute devices.\n";
  } else {
    log << "CUDA runtime version " << decodeVersion(runtimeVersion) << ", driver version "
        << decodeVersion(driverVersion) << ", NVIDIA driver version " << systemDriverVersion;
  }

  auto const& limits = config.getUntrackedParameter<edm::ParameterSet>("limits");
  auto printfFifoSize = limits.getUntrackedParameter<int>("cudaLimitPrintfFifoSize");
  auto stackSize = limits.getUntrackedParameter<int>("cudaLimitStackSize");
  auto mallocHeapSize = limits.getUntrackedParameter<int>("cudaLimitMallocHeapSize");
  auto devRuntimePendingLaunchCount = limits.getUntrackedParameter<int>("cudaLimitDevRuntimePendingLaunchCount");

  std::set<std::string> models;

  for (int i = 0; i < numberOfDevices_; ++i) {
    // read information about the compute device.
    // see the documentation of cudaGetDeviceProperties() for more information.
    cudaDeviceProp properties;
    CUDA_CHECK(cudaGetDeviceProperties(&properties, i));
    log << '\n' << "CUDA device " << i << ": " << properties.name;
    if (verbose_) {
      log << '\n';
    }
    models.insert(std::string(properties.name));

    // compute capabilities
    computeCapabilities_.emplace_back(properties.major, properties.minor);
    if (verbose_) {
      log << "  compute capability:          " << properties.major << "." << properties.minor;
    }
    log << " (sm_" << properties.major << properties.minor << ")";
    if (verbose_) {
      log << '\n';
      log << "  streaming multiprocessors: " << std::setw(13) << properties.multiProcessorCount << '\n';
      log << "  max threads per multiprocessor: " << std::setw(8) << properties.maxThreadsPerMultiProcessor << '\n';
      log << "  max resident threads: " << std::setw(18)
          << properties.multiProcessorCount * properties.maxThreadsPerMultiProcessor << '\n';
      auto const coresPerSM = getCudaCoresPerSM(properties.major, properties.minor);
      log << "  CUDA cores: " << std::setw(28) << properties.multiProcessorCount * coresPerSM << '\n';
      log << "  max resident threads per CUDA core: " << std::setw(4)
          << properties.maxThreadsPerMultiProcessor / getCudaCoresPerSM(properties.major, properties.minor) << '\n';
      int singleToDoublePrecisionPerfRatio = 0;
#if CUDART_VERSION < 13000
      singleToDoublePrecisionPerfRatio = properties.singleToDoublePrecisionPerfRatio;
#else
      CUDA_CHECK(
          cudaDeviceGetAttribute(&singleToDoublePrecisionPerfRatio, cudaDevAttrSingleToDoublePrecisionPerfRatio, i));
#endif
      log << "  single to double performance: " << std::setw(8) << singleToDoublePrecisionPerfRatio << ":1\n";
    }

    // compute mode
    static constexpr const char* computeModeDescription[] = {
        "default (shared)",            // cudaComputeModeDefault
        "exclusive (single thread)",   // cudaComputeModeExclusive
        "prohibited",                  // cudaComputeModeProhibited
        "exclusive (single process)",  // cudaComputeModeExclusiveProcess
        "unknown"};
    static constexpr const int cudaComputeModeUnknown = static_cast<int>(std::size(computeModeDescription)) - 1;
    if (verbose_) {
      int computeMode = cudaComputeModeUnknown;
#if CUDART_VERSION < 13000
      computeMode = properties.computeMode;
#else
      CUDA_CHECK(cudaDeviceGetAttribute(&computeMode, cudaDevAttrComputeMode, i));
#endif
      if (computeMode < 0 or computeMode >= cudaComputeModeUnknown) {
        computeMode = cudaComputeModeUnknown;
      }
      log << "  compute mode:" << std::right << std::setw(27) << computeModeDescription[computeMode] << '\n';
    }

    // TODO if a device is in exclusive use, skip it and remove it from the list, instead of failing with abort()
    CUDA_CHECK(cudaSetDevice(i));
    CUDA_CHECK(cudaSetDeviceFlags(cudaDeviceScheduleAuto | cudaDeviceMapHost));

    // read the free and total amount of memory available for allocation by the device, in bytes.
    // see the documentation of cudaMemGetInfo() for more information.
    if (verbose_) {
      size_t freeMemory, totalMemory;
      CUDA_CHECK(cudaMemGetInfo(&freeMemory, &totalMemory));
      log << "  memory: " << std::setw(6) << freeMemory / (1 << 20) << " MB free / " << std::setw(6)
          << totalMemory / (1 << 20) << " MB total\n";
      log << "  constant memory:               " << std::setw(6) << properties.totalConstMem / (1 << 10) << " kB\n";
      log << "  L2 cache size:                 " << std::setw(6) << properties.l2CacheSize / (1 << 10) << " kB\n";
    }

    // L1 cache behaviour
    if (verbose_) {
      static constexpr const char* l1CacheModeDescription[] = {
          "unknown", "local memory", "global memory", "local and global memory"};
      int l1CacheMode = properties.localL1CacheSupported + 2 * properties.globalL1CacheSupported;
      log << "  L1 cache mode:" << std::setw(26) << std::right << l1CacheModeDescription[l1CacheMode] << '\n';
      log << '\n';

      log << "Other capabilities\n";
      log << "  " << (properties.canMapHostMemory ? "can" : "cannot")
          << " map host memory into the CUDA address space for use with cudaHostAlloc()/cudaHostGetDevicePointer()\n";
      log << "  " << (properties.pageableMemoryAccess ? "supports" : "does not support")
          << " coherently accessing pageable memory without calling cudaHostRegister() on it\n";
      log << "  " << (properties.pageableMemoryAccessUsesHostPageTables ? "can" : "cannot")
          << " access pageable memory via the host's page tables\n";
      log << "  " << (properties.canUseHostPointerForRegisteredMem ? "can" : "cannot")
          << " access host registered memory at the same virtual address as the host\n";
      log << "  " << (properties.unifiedAddressing ? "shares" : "does not share")
          << " a unified address space with the host\n";
      log << "  " << (properties.managedMemory ? "supports" : "does not support")
          << " allocating managed memory on this system\n";
      log << "  " << (properties.concurrentManagedAccess ? "can" : "cannot")
          << " coherently access managed memory concurrently with the host\n";
      log << "  "
          << "the host " << (properties.directManagedMemAccessFromHost ? "can" : "cannot")
          << " directly access managed memory on the device without migration\n";
      log << "  " << (properties.cooperativeLaunch ? "supports" : "does not support")
          << " launching cooperative kernels via cudaLaunchCooperativeKernel()\n";
#if CUDART_VERSION < 13000
      log << "  " << (properties.cooperativeMultiDeviceLaunch ? "supports" : "does not support")
          << " launching cooperative kernels via cudaLaunchCooperativeKernelMultiDevice()\n";
#endif
      log << '\n';
    }

    // set and read the CUDA device flags.
    // see the documentation of cudaSetDeviceFlags and cudaGetDeviceFlags for  more information.
    if (verbose_) {
      log << "CUDA flags\n";
      unsigned int flags;
      CUDA_CHECK(cudaGetDeviceFlags(&flags));
      switch (flags & cudaDeviceScheduleMask) {
        case cudaDeviceScheduleAuto:
          log << "  thread policy:                   default\n";
          break;
        case cudaDeviceScheduleSpin:
          log << "  thread policy:                      spin\n";
          break;
        case cudaDeviceScheduleYield:
          log << "  thread policy:                     yield\n";
          break;
        case cudaDeviceScheduleBlockingSync:
          log << "  thread policy:             blocking sync\n";
          break;
        default:
          log << "  thread policy:                 undefined\n";
      }
      if (flags & cudaDeviceMapHost) {
        log << "  pinned host memory allocations:  enabled\n";
      } else {
        log << "  pinned host memory allocations: disabled\n";
      }
      if (flags & cudaDeviceLmemResizeToMax) {
        log << "  kernel host memory reuse:        enabled\n";
      } else {
        log << "  kernel host memory reuse:       disabled\n";
      }
      log << '\n';
    }

    // set and read the CUDA resource limits.
    // see the documentation of cudaDeviceSetLimit() for more information.

    // cudaLimitPrintfFifoSize controls the size in bytes of the shared FIFO used by the
    // printf() device system call.
    if (printfFifoSize >= 0) {
      setCudaLimit(cudaLimitPrintfFifoSize, "cudaLimitPrintfFifoSize", printfFifoSize);
    }
    // cudaLimitStackSize controls the stack size in bytes of each GPU thread.
    if (stackSize >= 0) {
      setCudaLimit(cudaLimitStackSize, "cudaLimitStackSize", stackSize);
    }
    // cudaLimitMallocHeapSize controls the size in bytes of the heap used by the malloc()
    // and free() device system calls.
    if (mallocHeapSize >= 0) {
      setCudaLimit(cudaLimitMallocHeapSize, "cudaLimitMallocHeapSize", mallocHeapSize);
    }
    if ((properties.major > 3) or (properties.major == 3 and properties.minor >= 5)) {
      // cudaLimitDevRuntimePendingLaunchCount controls the maximum number of outstanding
      // device runtime launches that can be made from the current device.
      if (devRuntimePendingLaunchCount >= 0) {
        setCudaLimit(cudaLimitDevRuntimePendingLaunchCount,
                     "cudaLimitDevRuntimePendingLaunchCount",
                     devRuntimePendingLaunchCount);
      }
    }

    if (verbose_) {
      size_t value;
      log << "CUDA limits\n";
      CUDA_CHECK(cudaDeviceGetLimit(&value, cudaLimitPrintfFifoSize));
      log << "  printf buffer size:        " << std::setw(10) << value / (1 << 20) << " MB\n";
      CUDA_CHECK(cudaDeviceGetLimit(&value, cudaLimitStackSize));
      log << "  stack size:                " << std::setw(10) << value / (1 << 10) << " kB\n";
      CUDA_CHECK(cudaDeviceGetLimit(&value, cudaLimitMallocHeapSize));
      log << "  malloc heap size:          " << std::setw(10) << value / (1 << 20) << " MB\n";
      if ((properties.major > 3) or (properties.major == 3 and properties.minor >= 5)) {
        CUDA_CHECK(cudaDeviceGetLimit(&value, cudaLimitDevRuntimePendingLaunchCount));
        log << "  runtime pending launch count: " << std::setw(10) << value << '\n';
      }
    }
  }

  edm::Service<edm::ResourceInformation> resourceInformationService;
  if (resourceInformationService.isAvailable()) {
    std::vector<std::string> modelsV(models.begin(), models.end());
    resourceInformationService->setGPUModels(modelsV);
    std::string nvidiaDriverVersion{systemDriverVersion};
    resourceInformationService->setNvidiaDriverVersion(nvidiaDriverVersion);
    resourceInformationService->setCudaDriverVersion(driverVersion);
    resourceInformationService->setCudaRuntimeVersion(runtimeVersion);
  }

  // Make sure the caching allocators and stream/event caches are constructed before declaring successful construction
  if constexpr (cms::cuda::allocator::useCaching) {
    cms::cuda::allocator::cachingAllocatorsConstruct();
  }
  cms::cuda::getEventCache().clear();
  cms::cuda::getStreamCache().clear();

  if (verbose_) {
    log << '\n' << "CUDAService fully initialized";
  }
  enabled_ = true;

  // Preallocate buffers if asked to
  auto const& allocator = config.getUntrackedParameter<edm::ParameterSet>("allocator");
  devicePreallocate(numberOfDevices_, allocator.getUntrackedParameter<std::vector<unsigned int>>("devicePreallocate"));
  hostPreallocate(allocator.getUntrackedParameter<std::vector<unsigned int>>("hostPreallocate"));
}

CUDAService::~CUDAService() {
  if (enabled_) {
    // Explicitly destruct the allocator before the device resets below
    if constexpr (cms::cuda::allocator::useCaching) {
      cms::cuda::allocator::cachingAllocatorsFreeCached();
    }
    cms::cuda::getEventCache().clear();
    cms::cuda::getStreamCache().clear();

    for (int i = 0; i < numberOfDevices_; ++i) {
      CUDA_CHECK(cudaSetDevice(i));
      CUDA_CHECK(cudaDeviceSynchronize());
      // Explicitly destroys and cleans up all resources associated with the current device in the
      // current process. Any subsequent API call to this device will reinitialize the device.
      // Useful to check for memory leaks with `cuda-memcheck --tool memcheck --leak-check full`.
      cudaDeviceReset();
    }
  }
}

void CUDAService::fillDescriptions(edm::ConfigurationDescriptions& descriptions) {
  edm::ParameterSetDescription desc;
  desc.addUntracked<bool>("enabled", true);
  desc.addUntracked<bool>("verbose", false);

  edm::ParameterSetDescription limits;
  limits.addUntracked<int>("cudaLimitPrintfFifoSize", -1)
      ->setComment("Size in bytes of the shared FIFO used by the printf() device system call.");
  limits.addUntracked<int>("cudaLimitStackSize", -1)->setComment("Stack size in bytes of each GPU thread.");
  limits.addUntracked<int>("cudaLimitMallocHeapSize", -1)
      ->setComment("Size in bytes of the heap used by the malloc() and free() device system calls.");
  limits.addUntracked<int>("cudaLimitDevRuntimePendingLaunchCount", -1)
      ->setComment("Maximum number of outstanding device runtime launches that can be made from the current device.");
  desc.addUntracked<edm::ParameterSetDescription>("limits", limits)
      ->setComment(
          "See the documentation of cudaDeviceSetLimit for more information.\nSetting any of these options to -1 keeps "
          "the default value.");

  edm::ParameterSetDescription allocator;
  allocator.addUntracked<std::vector<unsigned int>>("devicePreallocate", std::vector<unsigned int>{})
      ->setComment("Preallocates buffers of given bytes on all devices");
  allocator.addUntracked<std::vector<unsigned int>>("hostPreallocate", std::vector<unsigned int>{})
      ->setComment("Preallocates buffers of given bytes on the host");
  desc.addUntracked<edm::ParameterSetDescription>("allocator", allocator);

  descriptions.add("CUDAService", desc);
}

#include "FWCore/ServiceRegistry/interface/ServiceMaker.h"
using CUDAServiceMaker = edm::serviceregistry::ParameterSetMaker<CUDAInterface, CUDAService>;
DEFINE_FWK_SERVICE_MAKER(CUDAService, CUDAServiceMaker);