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* Copyright (c) 2011-2022, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
******************************************************************************/
#include <cub/device/device_memcpy.cuh>
#include <cub/iterator/transform_input_iterator.cuh>
#include <cub/util_ptx.cuh>
#include <thrust/device_vector.h>
#include <thrust/fill.h>
#include <thrust/host_vector.h>
#include <thrust/iterator/zip_iterator.h>
#include <thrust/logical.h>
#include <thrust/sequence.h>
#include <algorithm>
#include <cstdint>
#include <limits>
#include <numeric>
#include <random>
#include <type_traits>
#include <vector>
#include "test_util.h"
/**
* @brief Host-side random data generation
*/
template <typename T>
void GenerateRandomData(
T *rand_out,
const std::size_t num_items,
const T min_rand_val = std::numeric_limits<T>::min(),
const T max_rand_val = std::numeric_limits<T>::max(),
const std::uint_fast32_t seed = 320981U,
typename std::enable_if<std::is_integral<T>::value && (sizeof(T) >= 2)>::type * = nullptr)
{
// initialize random number generator
std::mt19937 rng(seed);
std::uniform_int_distribution<T> uni_dist(min_rand_val, max_rand_val);
// generate random numbers
for (std::size_t i = 0; i < num_items; ++i)
{
rand_out[i] = uni_dist(rng);
}
}
template <typename InputBufferIt,
typename OutputBufferIt,
typename BufferSizeIteratorT,
typename BufferOffsetT>
void __global__ BaselineBatchMemCpyKernel(InputBufferIt input_buffer_it,
OutputBufferIt output_buffer_it,
BufferSizeIteratorT buffer_sizes,
BufferOffsetT num_buffers)
{
BufferOffsetT gtid = blockDim.x * blockIdx.x + threadIdx.x;
if (gtid >= num_buffers)
{
return;
}
for (BufferOffsetT i = 0; i < buffer_sizes[gtid]; i++)
{
reinterpret_cast<uint8_t *>(output_buffer_it[gtid])[i] =
reinterpret_cast<uint8_t *>(input_buffer_it[gtid])[i];
}
}
template <typename InputBufferIt, typename OutputBufferIt, typename BufferSizeIteratorT>
void InvokeBaselineBatchMemcpy(InputBufferIt input_buffer_it,
OutputBufferIt output_buffer_it,
BufferSizeIteratorT buffer_sizes,
uint32_t num_buffers)
{
constexpr uint32_t block_threads = 128U;
uint32_t num_blocks = (num_buffers + block_threads - 1) / block_threads;
BaselineBatchMemCpyKernel<<<num_blocks, block_threads>>>(input_buffer_it,
output_buffer_it,
buffer_sizes,
num_buffers);
}
template <typename InputBufferIt,
typename OutputBufferIt,
typename BufferSizeIteratorT,
typename BufferOffsetT>
void __global__ BaselineBatchMemCpyPerBlockKernel(InputBufferIt input_buffer_it,
OutputBufferIt output_buffer_it,
BufferSizeIteratorT buffer_sizes,
BufferOffsetT num_buffers)
{
BufferOffsetT gbid = blockIdx.x;
if (gbid >= num_buffers)
{
return;
}
for (BufferOffsetT i = threadIdx.x; i < buffer_sizes[gbid] / 8; i += blockDim.x)
{
reinterpret_cast<uint64_t *>(output_buffer_it[gbid])[i] =
reinterpret_cast<uint64_t *>(input_buffer_it[gbid])[i];
}
}
/**
* @brief Used for generating a shuffled but cohesive sequence of output-buffer offsets for the
* sequence of input-buffers.
*/
template <typename BufferOffsetT, typename ByteOffsetT, typename BufferSizeT>
std::vector<ByteOffsetT> GetShuffledBufferOffsets(const std::vector<BufferSizeT> &buffer_sizes,
const std::uint_fast32_t seed = 320981U)
{
BufferOffsetT num_buffers = static_cast<BufferOffsetT>(buffer_sizes.size());
// We're remapping the i-th buffer to pmt_idxs[i]
std::mt19937 rng(seed);
std::vector<BufferOffsetT> pmt_idxs(num_buffers);
std::iota(pmt_idxs.begin(), pmt_idxs.end(), static_cast<BufferOffsetT>(0));
std::shuffle(std::begin(pmt_idxs), std::end(pmt_idxs), rng);
// Compute the offsets using the new mapping
ByteOffsetT running_offset = {};
std::vector<ByteOffsetT> permuted_offsets;
permuted_offsets.reserve(num_buffers);
for (auto permuted_buffer_idx : pmt_idxs)
{
permuted_offsets.emplace_back(running_offset);
running_offset += buffer_sizes[permuted_buffer_idx];
}
// Generate the scatter indexes that identify where each buffer was mapped to
std::vector<BufferOffsetT> scatter_idxs(num_buffers);
for (BufferOffsetT i = 0; i < num_buffers; i++)
{
scatter_idxs[pmt_idxs[i]] = i;
}
std::vector<ByteOffsetT> new_offsets(num_buffers);
for (BufferOffsetT i = 0; i < num_buffers; i++)
{
new_offsets[i] = permuted_offsets[scatter_idxs[i]];
}
return new_offsets;
}
/**
* @brief Function object class template that takes an offset and returns an iterator at the given
* offset relative to a fixed base iterator.
*
* @tparam IteratorT The random-access iterator type to be returned
*/
template <typename IteratorT>
struct OffsetToPtrOp
{
template <typename T>
__host__ __device__ __forceinline__ IteratorT operator()(T offset) const
{
return base_it + offset;
}
IteratorT base_it;
};
enum class TestDataGen
{
// Random offsets into a data segment
RANDOM,
// Buffers cohesively reside next to each other
CONSECUTIVE
};
/**
* @brief
*
* @tparam AtomicT The most granular type being copied. All source and destination pointers will be
* aligned based on this type, the number of bytes being copied will be an integer multiple of this
* type's size
* @tparam BufferOffsetT Type used for indexing into the array of buffers
* @tparam BufferSizeT Type used for indexing into individual bytes of a buffer (large enough to
* cover the max buffer size)
* @tparam ByteOffsetT Type used for indexing into bytes over *all* the buffers' sizes
*/
template <typename AtomicT, typename BufferOffsetT, typename BufferSizeT, typename ByteOffsetT>
void RunTest(BufferOffsetT num_buffers,
BufferSizeT min_buffer_size,
BufferSizeT max_buffer_size,
TestDataGen input_gen,
TestDataGen output_gen)
{
using SrcPtrT = uint8_t *;
// Buffer segment data (their offsets and sizes)
std::vector<BufferSizeT> h_buffer_sizes(num_buffers);
std::vector<ByteOffsetT> h_buffer_src_offsets(num_buffers);
std::vector<ByteOffsetT> h_buffer_dst_offsets(num_buffers);
// Device-side resources
void *d_in = nullptr;
void *d_out = nullptr;
ByteOffsetT *d_buffer_src_offsets = nullptr;
ByteOffsetT *d_buffer_dst_offsets = nullptr;
BufferSizeT *d_buffer_sizes = nullptr;
void *d_temp_storage = nullptr;
size_t temp_storage_bytes = 0;
// Generate the buffer sizes
GenerateRandomData(h_buffer_sizes.data(), h_buffer_sizes.size(), min_buffer_size, max_buffer_size);
// Make sure buffer sizes are a multiple of the most granular unit (one AtomicT) being copied
// (round down)
for (BufferOffsetT i = 0; i < num_buffers; i++)
{
h_buffer_sizes[i] = (h_buffer_sizes[i] / sizeof(AtomicT)) * sizeof(AtomicT);
}
// Compute the total bytes to be copied
ByteOffsetT num_total_bytes = 0;
for (BufferOffsetT i = 0; i < num_buffers; i++)
{
if (input_gen == TestDataGen::CONSECUTIVE)
{
h_buffer_src_offsets[i] = num_total_bytes;
}
if (output_gen == TestDataGen::CONSECUTIVE)
{
h_buffer_dst_offsets[i] = num_total_bytes;
}
num_total_bytes += h_buffer_sizes[i];
}
// Shuffle input buffer source-offsets
std::uint_fast32_t shuffle_seed = 320981U;
if (input_gen == TestDataGen::RANDOM)
{
h_buffer_src_offsets = GetShuffledBufferOffsets<BufferOffsetT, ByteOffsetT>(h_buffer_sizes,
shuffle_seed);
shuffle_seed += 42;
}
// Shuffle input buffer source-offsets
if (output_gen == TestDataGen::RANDOM)
{
h_buffer_dst_offsets = GetShuffledBufferOffsets<BufferOffsetT, ByteOffsetT>(h_buffer_sizes,
shuffle_seed);
}
// Get temporary storage requirements
CubDebugExit(cub::DeviceMemcpy::Batched(d_temp_storage,
temp_storage_bytes,
static_cast<SrcPtrT *>(nullptr),
static_cast<SrcPtrT *>(nullptr),
d_buffer_sizes,
num_buffers));
// Check if there's sufficient device memory to run this test
std::size_t total_required_mem = num_total_bytes + //
num_total_bytes + //
(num_buffers * sizeof(d_buffer_src_offsets[0])) + //
(num_buffers * sizeof(d_buffer_dst_offsets[0])) + //
(num_buffers * sizeof(d_buffer_sizes[0])) + //
temp_storage_bytes; //
if (TotalGlobalMem() < total_required_mem)
{
std::cout
<< "Skipping the test due to insufficient device memory\n" //
<< " - Required: " << total_required_mem << " B, available: " << TotalGlobalMem() << " B\n" //
<< " - Skipped test instance: " //
<< " -> Min. buffer size: " << min_buffer_size << ", max. buffer size: " << max_buffer_size //
<< ", num_buffers: " << num_buffers //
<< ", in_gen: " << ((input_gen == TestDataGen::RANDOM) ? "SHFL" : "CONSECUTIVE") //
<< ", out_gen: " << ((output_gen == TestDataGen::RANDOM) ? "SHFL" : "CONSECUTIVE");
return;
}
cudaEvent_t events[2];
cudaEventCreate(&events[0]);
cudaEventCreate(&events[1]);
cudaStream_t stream;
cudaStreamCreate(&stream);
// Allocate device memory
CubDebugExit(cudaMalloc(&d_in, num_total_bytes));
CubDebugExit(cudaMalloc(&d_out, num_total_bytes));
CubDebugExit(cudaMalloc(&d_buffer_src_offsets, num_buffers * sizeof(d_buffer_src_offsets[0])));
CubDebugExit(cudaMalloc(&d_buffer_dst_offsets, num_buffers * sizeof(d_buffer_dst_offsets[0])));
CubDebugExit(cudaMalloc(&d_buffer_sizes, num_buffers * sizeof(d_buffer_sizes[0])));
CubDebugExit(cudaMalloc(&d_temp_storage, temp_storage_bytes));
// Populate the data source with random data
using RandomInitAliasT = uint16_t;
std::size_t num_aliased_factor = sizeof(RandomInitAliasT) / sizeof(uint8_t);
std::size_t num_aliased_units = CUB_QUOTIENT_CEILING(num_total_bytes, num_aliased_factor);
std::unique_ptr<uint8_t[]> h_in(new uint8_t[num_aliased_units * num_aliased_factor]);
std::unique_ptr<uint8_t[]> h_out(new uint8_t[num_total_bytes]);
std::unique_ptr<uint8_t[]> h_gpu_results(new uint8_t[num_total_bytes]);
// Generate random offsets into the random-bits data buffer
GenerateRandomData(reinterpret_cast<RandomInitAliasT *>(h_in.get()), num_aliased_units);
// Prepare d_buffer_srcs
OffsetToPtrOp<SrcPtrT> src_transform_op{static_cast<SrcPtrT>(d_in)};
cub::TransformInputIterator<SrcPtrT, OffsetToPtrOp<SrcPtrT>, ByteOffsetT *> d_buffer_srcs(
d_buffer_src_offsets,
src_transform_op);
// Prepare d_buffer_dsts
OffsetToPtrOp<SrcPtrT> dst_transform_op{static_cast<SrcPtrT>(d_out)};
cub::TransformInputIterator<SrcPtrT, OffsetToPtrOp<SrcPtrT>, ByteOffsetT *> d_buffer_dsts(
d_buffer_dst_offsets,
dst_transform_op);
// Prepare random data segment (which serves for the buffer sources)
CubDebugExit(cudaMemcpyAsync(d_in, h_in.get(), num_total_bytes, cudaMemcpyHostToDevice, stream));
// Prepare d_buffer_src_offsets
CubDebugExit(cudaMemcpyAsync(d_buffer_src_offsets,
h_buffer_src_offsets.data(),
h_buffer_src_offsets.size() * sizeof(h_buffer_src_offsets[0]),
cudaMemcpyHostToDevice,
stream));
// Prepare d_buffer_dst_offsets
CubDebugExit(cudaMemcpyAsync(d_buffer_dst_offsets,
h_buffer_dst_offsets.data(),
h_buffer_dst_offsets.size() * sizeof(h_buffer_dst_offsets[0]),
cudaMemcpyHostToDevice,
stream));
// Prepare d_buffer_sizes
CubDebugExit(cudaMemcpyAsync(d_buffer_sizes,
h_buffer_sizes.data(),
h_buffer_sizes.size() * sizeof(h_buffer_sizes[0]),
cudaMemcpyHostToDevice,
stream));
// Record event before algorithm
cudaEventRecord(events[0], stream);
// Invoke device-side algorithm being under test
CubDebugExit(cub::DeviceMemcpy::Batched(d_temp_storage,
temp_storage_bytes,
d_buffer_srcs,
d_buffer_dsts,
d_buffer_sizes,
num_buffers,
stream));
// Record event after algorithm
cudaEventRecord(events[1], stream);
// Copy back the output buffer
CubDebugExit(
cudaMemcpyAsync(h_gpu_results.get(), d_out, num_total_bytes, cudaMemcpyDeviceToHost, stream));
// Make sure results have been copied back to the host
CubDebugExit(cudaStreamSynchronize(stream));
// CPU-side result generation for verification
for (BufferOffsetT i = 0; i < num_buffers; i++)
{
std::memcpy(h_out.get() + h_buffer_dst_offsets[i],
h_in.get() + h_buffer_src_offsets[i],
h_buffer_sizes[i]);
}
float duration = 0;
cudaEventElapsedTime(&duration, events[0], events[1]);
#ifdef CUB_TEST_BENCHMARK
size_t stats_src_offsets = sizeof(ByteOffsetT) * num_buffers;
size_t stats_dst_offsets = sizeof(ByteOffsetT) * num_buffers;
size_t stats_sizes = sizeof(BufferSizeT) * num_buffers;
size_t stats_data_copied = 2 * num_total_bytes;
std::cout
<< "Min. buffer size: " << min_buffer_size << ", max. buffer size: " << max_buffer_size //
<< ", num_buffers: " << num_buffers //
<< ", in_gen: " << ((input_gen == TestDataGen::RANDOM) ? "SHFL" : "CONSECUTIVE") //
<< ", out_gen: " << ((output_gen == TestDataGen::RANDOM) ? "SHFL" : "CONSECUTIVE") //
<< ", src size: " << stats_src_offsets << ", dst size: " << stats_dst_offsets //
<< ", sizes size: " << stats_sizes << ", cpy_data_size: " << stats_data_copied //
<< ", total: " << (stats_src_offsets + stats_dst_offsets + stats_sizes + stats_data_copied) //
<< ", duration: " << duration //
<< ", BW: "
<< ((double)(stats_src_offsets + stats_dst_offsets + stats_sizes + stats_data_copied) /
1000000000.0) /
(duration / 1000.0)
<< "GB/s \n";
#endif
for (ByteOffsetT i = 0; i < num_total_bytes; i++)
{
if (h_gpu_results.get()[i] != h_out.get()[i])
{
std::cout << "Mismatch at index " << i
<< ", CPU vs. GPU: " << static_cast<uint16_t>(h_gpu_results.get()[i]) << ", "
<< static_cast<uint16_t>(h_out.get()[i]) << "\n";
}
AssertEquals(h_out.get()[i], h_gpu_results.get()[i]);
}
CubDebugExit(cudaFree(d_in));
CubDebugExit(cudaFree(d_out));
CubDebugExit(cudaFree(d_buffer_src_offsets));
CubDebugExit(cudaFree(d_buffer_dst_offsets));
CubDebugExit(cudaFree(d_buffer_sizes));
CubDebugExit(cudaFree(d_temp_storage));
}
template <int LOGICAL_WARP_SIZE, typename VectorT, typename ByteOffsetT>
__global__ void TestVectorizedCopyKernel(const void *d_in, void *d_out, ByteOffsetT copy_size)
{
cub::detail::VectorizedCopy<LOGICAL_WARP_SIZE, VectorT>(threadIdx.x, d_out, copy_size, d_in);
}
struct TupleMemberEqualityOp
{
template <typename T>
__host__ __device__ __forceinline__ bool operator()(T tuple)
{
return thrust::get<0>(tuple) == thrust::get<1>(tuple);
}
};
/**
* @brief Tests the VectorizedCopy for various aligned and misaligned input and output pointers.
* @tparam VectorT The vector type used for vectorized stores (i.e., one of uint4, uint2, uint32_t)
*/
template <typename VectorT>
void TestVectorizedCopy()
{
constexpr uint32_t threads_per_block = 8;
std::vector<std::size_t> in_offsets{0, 1, sizeof(uint32_t) - 1};
std::vector<std::size_t> out_offsets{0, 1, sizeof(VectorT) - 1};
std::vector<std::size_t> copy_sizes{0,
1,
sizeof(uint32_t),
sizeof(VectorT),
2 * threads_per_block * sizeof(VectorT)};
for (auto copy_sizes_it = std::begin(copy_sizes); copy_sizes_it < std::end(copy_sizes);
copy_sizes_it++)
{
for (auto in_offsets_it = std::begin(in_offsets); in_offsets_it < std::end(in_offsets);
in_offsets_it++)
{
for (auto out_offsets_it = std::begin(out_offsets); out_offsets_it < std::end(out_offsets);
out_offsets_it++)
{
std::size_t in_offset = *in_offsets_it;
std::size_t out_offset = *out_offsets_it;
std::size_t copy_size = *copy_sizes_it;
// Prepare data
const std::size_t alloc_size_in = in_offset + copy_size;
const std::size_t alloc_size_out = out_offset + copy_size;
thrust::device_vector<char> data_in(alloc_size_in);
thrust::device_vector<char> data_out(alloc_size_out);
thrust::sequence(data_in.begin(), data_in.end(), static_cast<char>(0));
thrust::fill_n(data_out.begin(), alloc_size_out, static_cast<char>(0x42));
auto d_in = thrust::raw_pointer_cast(data_in.data());
auto d_out = thrust::raw_pointer_cast(data_out.data());
TestVectorizedCopyKernel<threads_per_block, VectorT>
<<<1, threads_per_block>>>(d_in + in_offset,
d_out + out_offset,
static_cast<int>(copy_size));
auto zip_it = thrust::make_zip_iterator(data_in.begin() + in_offset,
data_out.begin() + out_offset);
bool success = thrust::all_of(zip_it, zip_it + copy_size, TupleMemberEqualityOp{});
AssertTrue(success);
}
}
}
}
template <uint32_t NUM_ITEMS, uint32_t MAX_ITEM_VALUE, bool PREFER_POW2_BITS>
__global__ void TestBitPackedCounterKernel(uint32_t *bins,
uint32_t *increments,
uint32_t *counts_out,
uint32_t num_items)
{
using BitPackedCounterT =
cub::detail::BitPackedCounter<NUM_ITEMS, MAX_ITEM_VALUE, PREFER_POW2_BITS>;
BitPackedCounterT counter{};
for (uint32_t i = 0; i < num_items; i++)
{
counter.Add(bins[i], increments[i]);
}
for (uint32_t i = 0; i < NUM_ITEMS; i++)
{
counts_out[i] = counter.Get(i);
}
}
/**
* @brief Tests BitPackedCounter that's used for computing the histogram of buffer sizes (i.e.,
* small, medium, large).
*/
template <uint32_t NUM_ITEMS, uint32_t MAX_ITEM_VALUE>
void TestBitPackedCounter(const std::uint_fast32_t seed = 320981U)
{
constexpr uint32_t min_increment = 0;
constexpr uint32_t max_increment = 4;
constexpr double avg_increment = static_cast<double>(min_increment) +
(static_cast<double>(max_increment - min_increment) / 2.0);
std::uint32_t num_increments =
static_cast<uint32_t>(static_cast<double>(MAX_ITEM_VALUE * NUM_ITEMS) / avg_increment);
// Test input data
std::array<uint64_t, NUM_ITEMS> reference_counters{};
thrust::host_vector<uint32_t> h_bins(num_increments);
thrust::host_vector<uint32_t> h_increments(num_increments);
// Generate random test input data
GenerateRandomData(thrust::raw_pointer_cast(h_bins.data()),
num_increments,
0U,
NUM_ITEMS - 1U,
seed);
GenerateRandomData(thrust::raw_pointer_cast(h_increments.data()),
num_increments,
min_increment,
max_increment,
(seed + 17));
// Make sure test data does not overflow any of the counters
for (std::size_t i = 0; i < num_increments; i++)
{
// New increment for this bin would overflow => zero this increment
if (reference_counters[h_bins[i]] + h_increments[i] >= MAX_ITEM_VALUE)
{
h_increments[i] = 0;
}
else
{
reference_counters[h_bins[i]] += h_increments[i];
}
}
// Device memory
thrust::device_vector<uint32_t> bins_in(num_increments);
thrust::device_vector<uint32_t> increments_in(num_increments);
thrust::device_vector<uint32_t> counts_out(NUM_ITEMS);
// Initialize device-side test data
bins_in = h_bins;
increments_in = h_increments;
// Memory for GPU-generated results
thrust::host_vector<uint32_t> host_counts(num_increments);
// Reset counters to arbitrary random value
thrust::fill(counts_out.begin(), counts_out.end(), 814920U);
// Run tests with densely bit-packed counters
TestBitPackedCounterKernel<NUM_ITEMS, MAX_ITEM_VALUE, false>
<<<1, 1>>>(thrust::raw_pointer_cast(bins_in.data()),
thrust::raw_pointer_cast(increments_in.data()),
thrust::raw_pointer_cast(counts_out.data()),
num_increments);
// Result verification
host_counts = counts_out;
for (uint32_t i = 0; i < NUM_ITEMS; i++)
{
AssertEquals(reference_counters[i], host_counts[i]);
}
// Reset counters to arbitrary random value
thrust::fill(counts_out.begin(), counts_out.end(), 814920U);
// Run tests with bit-packed counters, where bit-count is a power-of-two
TestBitPackedCounterKernel<NUM_ITEMS, MAX_ITEM_VALUE, true>
<<<1, 1>>>(thrust::raw_pointer_cast(bins_in.data()),
thrust::raw_pointer_cast(increments_in.data()),
thrust::raw_pointer_cast(counts_out.data()),
num_increments);
// Result verification
host_counts = counts_out;
for (uint32_t i = 0; i < NUM_ITEMS; i++)
{
AssertEquals(reference_counters[i], host_counts[i]);
}
}
int main(int argc, char **argv)
{
CommandLineArgs args(argc, argv);
// Initialize device
CubDebugExit(args.DeviceInit());
//---------------------------------------------------------------------
// VectorizedCopy tests
//---------------------------------------------------------------------
TestVectorizedCopy<uint32_t>();
TestVectorizedCopy<uint4>();
//---------------------------------------------------------------------
// BitPackedCounter tests
//---------------------------------------------------------------------
TestBitPackedCounter<1, 1>();
TestBitPackedCounter<1, (0x01U << 16)>();
TestBitPackedCounter<4, 1>();
TestBitPackedCounter<4, 2>();
TestBitPackedCounter<4, 255>();
TestBitPackedCounter<4, 256>();
TestBitPackedCounter<8, 1024>();
TestBitPackedCounter<32, 1>();
TestBitPackedCounter<32, 256>();
//---------------------------------------------------------------------
// DeviceMemcpy::Batched tests
//---------------------------------------------------------------------
// The most granular type being copied. Buffer's will be aligned and their size be an integer
// multiple of this type
using AtomicCopyT = uint8_t;
// Type used for indexing into the array of buffers
using BufferOffsetT = uint32_t;
// Type used for indexing into individual bytes of a buffer (large enough to cover the max buffer
using BufferSizeT = uint32_t;
// Type used for indexing into bytes over *all* the buffers' sizes
using ByteOffsetT = uint32_t;
// Total number of bytes that are targeted to be copied on each run
const BufferOffsetT target_copy_size = 64U << 20;
// The number of randomly
constexpr std::size_t num_rnd_buffer_range_tests = 32;
// Each buffer's size will be random within this interval
std::vector<std::pair<std::size_t, std::size_t>> buffer_size_ranges = {{0, 1},
{1, 2},
{0, 16},
{1, 32},
{1, 1024},
{1, 32 * 1024},
{128 * 1024, 256 * 1024},
{target_copy_size,
target_copy_size}};
std::mt19937 rng(0);
std::uniform_int_distribution<std::size_t> size_dist(1, 1000000);
for (std::size_t i = 0; i < num_rnd_buffer_range_tests; i++)
{
auto range_begin = size_dist(rng);
auto range_end = size_dist(rng);
if (range_begin > range_end)
{
std::swap(range_begin, range_end);
}
buffer_size_ranges.push_back({range_begin, range_end});
}
for (const auto &buffer_size_range : buffer_size_ranges)
{
BufferSizeT min_buffer_size =
static_cast<BufferSizeT>(CUB_ROUND_UP_NEAREST(buffer_size_range.first, sizeof(AtomicCopyT)));
BufferSizeT max_buffer_size =
static_cast<BufferSizeT>(CUB_ROUND_UP_NEAREST(buffer_size_range.second,
static_cast<BufferSizeT>(sizeof(AtomicCopyT))));
double average_buffer_size = (min_buffer_size + max_buffer_size) / 2.0;
BufferOffsetT target_num_buffers =
static_cast<BufferOffsetT>(target_copy_size / average_buffer_size);
// Run tests with input buffer being consecutive and output buffers being consecutive
RunTest<AtomicCopyT, BufferOffsetT, BufferSizeT, ByteOffsetT>(target_num_buffers,
min_buffer_size,
max_buffer_size,
TestDataGen::CONSECUTIVE,
TestDataGen::CONSECUTIVE);
// Run tests with input buffer being randomly shuffled and output buffers being randomly
// shuffled
RunTest<AtomicCopyT, BufferOffsetT, BufferSizeT, ByteOffsetT>(target_num_buffers,
min_buffer_size,
max_buffer_size,
TestDataGen::RANDOM,
TestDataGen::RANDOM);
}
//---------------------------------------------------------------------
// DeviceMemcpy::Batched test with 64-bit offsets
//---------------------------------------------------------------------
using ByteOffset64T = uint64_t;
using BufferSize64T = uint64_t;
ByteOffset64T large_target_copy_size =
static_cast<ByteOffset64T>(std::numeric_limits<uint32_t>::max()) + (128ULL * 1024ULL * 1024ULL);
// Make sure min_buffer_size is in fact smaller than max buffer size
constexpr BufferOffsetT single_buffer = 1;
// Run tests with input buffer being consecutive and output buffers being consecutive
RunTest<AtomicCopyT, BufferOffsetT, BufferSize64T, ByteOffset64T>(single_buffer,
large_target_copy_size,
large_target_copy_size,
TestDataGen::CONSECUTIVE,
TestDataGen::CONSECUTIVE);
}
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