Move host-side allocation to benchmarks and reuse device with UVM

This commit puts benchmarks in control of allocating the host
memory used for verifying the results.

This enables benchmarks that use Unified Memory for the device
allocations, to avoid the host-side allocation and just pass
pointers to the device allocation to the benchmark driver.

Closes UoB-HPC#128 .

CMakeLists.txt

@@ -44,9 +44,14 @@ if ((NOT BUILD_TYPE STREQUAL RELEASE) AND (NOT BUILD_TYPE STREQUAL DEBUG))
     message(FATAL_ERROR "Only Release or Debug is supported, got `${CMAKE_BUILD_TYPE}`")
 endif ()
 
+option(BUILD_NATIVE "Builds for the current systems CPU and GPU architecture." ON)
+
 # setup some defaults flags for everything
 set(DEFAULT_DEBUG_FLAGS -O2 -fno-omit-frame-pointer)
-set(DEFAULT_RELEASE_FLAGS -O3 -march=native)
+set(DEFAULT_RELEASE_FLAGS -O3)
+if (BUILD_NATIVE)
+    set(DEFAULT_RELEASE_FLAGS ${DEFAULT_RELEASE_FLAGS} -march=native)
+endif()
 
 macro(hint_flag FLAG DESCRIPTION)
     if (NOT DEFINED ${FLAG})

src/Stream.h

@@ -7,14 +7,10 @@
 
 #pragma once
 
+#include <array>
 #include <vector>
 #include <string>
-
-// Array values
-#define startA (0.1)
-#define startB (0.2)
-#define startC (0.0)
-#define startScalar (0.4)
+#include "benchmark.h"
 
 template <class T>
 class Stream
@@ -31,9 +27,8 @@ class Stream
     virtual void nstream() = 0;
     virtual T dot() = 0;
 
-    // Copy memory between host and device
-    virtual void init_arrays(T initA, T initB, T initC) = 0;
-    virtual void read_arrays(std::vector<T>& a, std::vector<T>& b, std::vector<T>& c) = 0;
+    // Set pointers to read from arrays
+    virtual void get_arrays(T const*& a, T const*& b, T const*& c) = 0;
 };
 
 // Implementation specific device functions

src/StreamModels.h

@@ -35,67 +35,67 @@
 #include "FutharkStream.h"
 #endif
 
-template <typename T>
-std::unique_ptr<Stream<T>> make_stream(intptr_t array_size, int deviceIndex) {
+template <typename T, typename...Args>
+std::unique_ptr<Stream<T>> make_stream(Args... args) {
 #if defined(CUDA)
   // Use the CUDA implementation
-  return std::make_unique<CUDAStream<T>>(array_size, deviceIndex);
+  return std::make_unique<CUDAStream<T>>(args...);
 
 #elif defined(HIP)
   // Use the HIP implementation
-  return std::make_unique<HIPStream<T>>(array_size, deviceIndex);
+  return std::make_unique<HIPStream<T>>(args...);
 
 #elif defined(HC)
   // Use the HC implementation
-  return std::make_unique<HCStream<T>>(array_size, deviceIndex);
+  return std::make_unique<HCStream<T>>(args...);
 
 #elif defined(OCL)
   // Use the OpenCL implementation
-  return std::make_unique<OCLStream<T>>(array_size, deviceIndex);
+  return std::make_unique<OCLStream<T>>(args...);
 
 #elif defined(USE_RAJA)
   // Use the RAJA implementation
-  return std::make_unique<RAJAStream<T>>(array_size, deviceIndex);
+  return std::make_unique<RAJAStream<T>>(args...);
 
 #elif defined(KOKKOS)
   // Use the Kokkos implementation
-  return std::make_unique<KokkosStream<T>>(array_size, deviceIndex);
+  return std::make_unique<KokkosStream<T>>(args...);
 
 #elif defined(STD_DATA)
   // Use the C++ STD data-oriented implementation
-  return std::make_unique<STDDataStream<T>>(array_size, deviceIndex);
+  return std::make_unique<STDDataStream<T>>(args...);
 
 #elif defined(STD_INDICES)
   // Use the C++ STD index-oriented implementation
-  return std::make_unique<STDIndicesStream<T>>(array_size, deviceIndex);
+  return std::make_unique<STDIndicesStream<T>>(args...);
 
 #elif defined(STD_RANGES)
   // Use the C++ STD ranges implementation
-  return std::make_unique<STDRangesStream<T>>(array_size, deviceIndex);
+  return std::make_unique<STDRangesStream<T>>(args...);
 
 #elif defined(TBB)
   // Use the C++20 implementation
-  return std::make_unique<TBBStream<T>>(array_size, deviceIndex);
+  return std::make_unique<TBBStream<T>>(args...);
 
 #elif defined(THRUST)
   // Use the Thrust implementation
-  return std::make_unique<ThrustStream<T>>(array_size, deviceIndex);
+  return std::make_unique<ThrustStream<T>>(args...);
 
 #elif defined(ACC)
   // Use the OpenACC implementation
-  return std::make_unique<ACCStream<T>>(array_size, deviceIndex);
+  return std::make_unique<ACCStream<T>>(args...);
 
 #elif defined(SYCL) || defined(SYCL2020)
   // Use the SYCL implementation
-  return std::make_unique<SYCLStream<T>>(array_size, deviceIndex);
+  return std::make_unique<SYCLStream<T>>(args...);
 
 #elif defined(OMP)
   // Use the OpenMP implementation
-  return std::make_unique<OMPStream<T>>(array_size, deviceIndex);
+  return std::make_unique<OMPStream<T>>(args...);
 
 #elif defined(FUTHARK)
   // Use the Futhark implementation
-  return std::make_unique<FutharkStream<T>>(array_size, deviceIndex);
+  return std::make_unique<FutharkStream<T>>(args...);
 
 #else
 

src/acc/ACCStream.cpp

@@ -8,11 +8,12 @@
 #include "ACCStream.h"
 
 template <class T>
-ACCStream<T>::ACCStream(const intptr_t ARRAY_SIZE, int device)
-  : array_size{ARRAY_SIZE}
+ACCStream<T>::ACCStream(BenchId bs, const intptr_t array_size, const int device_id,
+			T initA, T initB, T initC)
+  : array_size{array_size}
 {
   acc_device_t device_type = acc_get_device_type();
-  acc_set_device_num(device, device_type);
+  acc_set_device_num(device_id, device_type);
 
   // Set up data region on device
   this->a = new T[array_size];
@@ -25,6 +26,8 @@ ACCStream<T>::ACCStream(const intptr_t ARRAY_SIZE, int device)
 
   #pragma acc enter data create(a[0:array_size], b[0:array_size], c[0:array_size])
   {}
+
+  init_arrays(initA, initB, initC);
 }
 
 template <class T>
@@ -62,20 +65,17 @@ void ACCStream<T>::init_arrays(T initA, T initB, T initC)
 }
 
 template <class T>
-void ACCStream<T>::read_arrays(std::vector<T>& h_a, std::vector<T>& h_b, std::vector<T>& h_c)
+void ACCStream<T>::get_arrays(T const*& h_a, T const*& h_b, T const*& h_c)
 {
   T *a = this->a;
   T *b = this->b;
   T *c = this->c;
   #pragma acc update host(a[0:array_size], b[0:array_size], c[0:array_size])
   {}
 
-  for (intptr_t i = 0; i < array_size; i++)
-  {
-    h_a[i] = a[i];
-    h_b[i] = b[i];
-    h_c[i] = c[i];
-  }
+  h_a = a;
+  h_b = b;
+  h_c = c;
 }
 
 template <class T>

src/acc/ACCStream.h

@@ -19,32 +19,25 @@
 template <class T>
 class ACCStream : public Stream<T>
 {
-  struct A{
-    T *a;
-    T *b;
-    T *c;
-  };
-
-  protected:
     // Size of arrays
     intptr_t array_size;
-    A aa;
     // Device side pointers
-    T *a;
-    T *b;
-    T *c;
+    T* restrict a;
+    T* restrict b;
+    T* restrict c;
 
   public:
-    ACCStream(const intptr_t, int);
+    ACCStream(BenchId bs, const intptr_t array_size, const int device_id,
+	      T initA, T initB, T initC);
     ~ACCStream();
 
-    virtual void copy() override;
-    virtual void add() override;
-    virtual void mul() override;
-    virtual void triad() override;
-    virtual void nstream() override;
-    virtual T dot() override;
+    void copy() override;
+    void add() override;
+    void mul() override;
+    void triad() override;
+    void nstream() override;
+    T dot() override;
 
-    virtual void init_arrays(T initA, T initB, T initC) override;
-    virtual void read_arrays(std::vector<T>& a, std::vector<T>& b, std::vector<T>& c) override;
+    void get_arrays(T const*& a, T const*& b, T const*& c) override;
+    void init_arrays(T initA, T initB, T initC);
 };

src/benchmark.h

@@ -0,0 +1,66 @@
+#pragma once
+
+#include <algorithm>
+#include <array>
+#include <initializer_list>
+#include <iostream>
+
+// Array values
+#define startA (0.1)
+#define startB (0.2)
+#define startC (0.0)
+#define startScalar (0.4)
+
+// Benchmark Identifier: identifies individual & groups of benchmarks:
+// - Classic: 5 classic kernels: Copy, Mul, Add, Triad, Dot.
+// - All: all kernels.
+// - Individual kernels only.
+enum class BenchId : int {Copy, Mul, Add, Triad, Nstream, Dot, Classic, All};
+
+struct Benchmark {
+  BenchId id;
+  char const* label;
+  // Weight counts data elements of original arrays moved each loop iteration - used to calculate achieved BW:
+  // bytes = weight * sizeof(T) * ARRAY_SIZE -> bw = bytes / dur
+  size_t weight;
+  // Is it one of: Copy, Mul, Add, Triad, Dot?
+  bool classic = false;
+};
+
+// Benchmarks in the order in which - if present - should be run for validation purposes:
+constexpr size_t num_benchmarks = 6;
+constexpr std::array<Benchmark, num_benchmarks> bench = {
+  Benchmark { .id = BenchId::Copy,    .label = "Copy",    .weight = 2, .classic = true  },
+  Benchmark { .id = BenchId::Mul,     .label = "Mul",     .weight = 2, .classic = true  },
+  Benchmark { .id = BenchId::Add,     .label = "Add",     .weight = 3, .classic = true  },
+  Benchmark { .id = BenchId::Triad,   .label = "Triad",   .weight = 3, .classic = true  },
+  Benchmark { .id = BenchId::Dot,     .label = "Dot",     .weight = 2, .classic = true  },
+  Benchmark { .id = BenchId::Nstream, .label = "Nstream", .weight = 4, .classic = false }
+};
+
+// Which buffers are needed by each benchmark
+inline bool needs_buffer(BenchId id, char n) {
+  auto in = [n](std::initializer_list<char> values) {
+    return std::find(values.begin(), values.end(), n) != values.end();
+  };
+  switch(id) {
+  case BenchId::All:     return in({'a','b','c'});       
+  case BenchId::Classic: return in({'a','b','c'});   
+  case BenchId::Copy:    return in({'a','c'});
+  case BenchId::Mul:	 return in({'b','c'});
+  case BenchId::Add:	 return in({'a','b','c'});
+  case BenchId::Triad:   return in({'a','b','c'});
+  case BenchId::Dot:	 return in({'a','b'});
+  case BenchId::Nstream: return in({'a','b','c'});  
+  default:
+    std::cerr << "Unknown benchmark" << std::endl;
+    abort();
+  }
+}
+
+// Returns true if the benchmark needs to be run:
+inline bool run_benchmark(BenchId selection, Benchmark const& b) {
+  if (selection == BenchId::All)                  return true;
+  if (selection == BenchId::Classic && b.classic) return true;
+  return selection == b.id;
+}

src/ci-prepare-bionic.sh

@@ -138,9 +138,9 @@ setup_aocc() {
 
 setup_nvhpc() {
   echo "Preparing Nvidia HPC SDK"
-  local nvhpc_ver="23.1" # TODO FIXME > 23.1 has a bug with -A
-  local nvhpc_release="2023_231"
-  local cuda_ver="12.0"
+  local nvhpc_ver="24.5"
+  local nvhpc_release="2024_245"
+  local cuda_ver="12.4"
 
   local tarball="nvhpc_$nvhpc_ver.tar.gz"
 
@@ -289,20 +289,18 @@ setup_dpcpp() {
   check_size
 }
 
-setup_hipsycl() {
+setup_adaptivecpp() {
 
   sudo apt-get install -y -qq libboost-fiber-dev libboost-context-dev
-  local hipsycl_ver="0.9.1"
-  local tarball="v$hipsycl_ver.tar.gz"
-  local install_dir="$PWD/hipsycl_dist_$hipsycl_ver"
-
-  local url="https://github.com/AdaptiveCpp/AdaptiveCpp/archive/v$hipsycl_ver.tar.gz"
-  # local url="http://localhost:8000/AdaptiveCpp-$hipsycl_ver.tar.gz"
+  local adaptivecpp_ver="24.02.0"
+  local tarball="adaptivecpp_v${adaptivecpp_ver}.tar.gz"
+  local install_dir="$PWD/adaptivecpp_dist_v${adaptivecpp_ver}"
+  local url="https://github.com/AdaptiveCpp/AdaptiveCpp/archive/refs/tags/v${adaptivecpp_ver}.tar.gz"
 
   get_and_untar "$tarball" "$url"
 
   if [ "$SETUP" = true ]; then
-    local src="$PWD/AdaptiveCpp-$hipsycl_ver"
+    local src="$PWD/AdaptiveCpp-v${adaptivecpp_ver}"
     rm -rf "$src/build"
     rm -rf "$install_dir"
     cmake "-B$src/build" "-H$src" \
@@ -315,10 +313,10 @@ setup_hipsycl() {
     cmake --build "$src/build" --target install -j "$(nproc)"
   fi
 
-  export_var HIPSYCL_DIR "$install_dir"
-  verify_dir_exists "$HIPSYCL_DIR"
-  # note: this will forward --version to the default compiler so it won't say anything about hipsycl
-  "$HIPSYCL_DIR/bin/syclcc-clang" --version
+  export_var AdaptiveCpp_DIR "$install_dir"
+  verify_dir_exists "$AdaptiveCpp_DIR"
+  # note: this will forward --version to the default compiler so it won't say anything about adaptivecpp
+  "$AdaptiveCpp_DIR/bin/syclcc-clang" --version
   check_size
 }
 
@@ -391,7 +389,7 @@ setup_cmake() {
 }
 
 if [ "$PARALLEL" = true ]; then
-  (setup_clang_gcc && setup_rocm && setup_hipsycl) &   # these need apt so run sequentially
+  (setup_clang_gcc && setup_rocm && setup_adaptivecpp) &   # these need apt so run sequentially
   setup_cmake &
   setup_oclcpu &
   setup_aocc &
@@ -406,7 +404,7 @@ else
   # these need apt
   setup_clang_gcc
   setup_rocm
-  setup_hipsycl
+  setup_adaptivecpp
   setup_cmake
   setup_aocc
   setup_oclcpu

src/ci-test-compile.sh

@@ -335,10 +335,10 @@ build_dpcpp() {
   #  run_build intel_build "dpcpp" sycl "-DCMAKE_CXX_COMPILER=${GCC_CXX:?} -DSYCL_COMPILER=ONEAPI-DPCPP"
 }
 
-build_hipsycl() {
-  run_build hipsycl_build "syclcc" sycl "
-  -DSYCL_COMPILER=HIPSYCL \
-  -DSYCL_COMPILER_DIR=${HIPSYCL_DIR:?}"
+build_adaptivecpp() {
+  run_build adaptivecpp_build "syclcc" sycl "
+  -DSYCL_COMPILER=AdaptiveCpp \
+  -DSYCL_COMPILER_DIR=${AdaptiveCpp_DIR:?}"
 }
 
 echo "Test compiling with ${COMPILER} CXX for ${MODEL} model"
@@ -352,7 +352,7 @@ aocc) build_aocc ;;
 aomp) build_aomp ;;
 hip) build_hip ;;
 dpcpp) build_dpcpp ;;
-hipsycl) build_hipsycl ;;
+adaptivecpp) build_adaptivecpp ;;
 
 # XXX below are local only; licence or very large download required, candidate for local runner
 icpx) build_icpx ;;
@@ -366,7 +366,7 @@ all)
   build_aomp
   build_hip
   build_dpcpp
-  build_hipsycl
+  build_adaptivecpp
 
   build_icpx
   build_icpc