56fd68e7dd
CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
123 lines
3.4 KiB
Plaintext
123 lines
3.4 KiB
Plaintext
//===----------------------------------------------------------------------===//
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//
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// Part of CUDASTF in CUDA C++ Core Libraries,
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// under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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#include <cuda/experimental/__stf/stream/interfaces/hashtable_linearprobing.cuh>
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#include <cuda/experimental/__stf/stream/reduction.cuh>
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#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
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using namespace cuda::experimental::stf;
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__global__ void gpu_merge_hashtable(hashtable A, const hashtable B)
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{
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unsigned int threadid = blockIdx.x * blockDim.x + threadIdx.x;
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while (threadid < B.get_capacity())
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{
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if (B.addr[threadid].key != reserved::kEmpty)
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{
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uint32_t value = B.addr[threadid].value;
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if (value != reserved::kEmpty)
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{
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// printf("INSERTING key %d value %d\n", pHashTableB[threadid].key, value);
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A.insert(B.addr[threadid]);
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}
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}
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threadid += blockDim.x * gridDim.x;
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}
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}
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void cpu_merge_hashtable(hashtable A, const hashtable B)
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{
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for (unsigned int i = 0; i < B.get_capacity(); i++)
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{
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if (B.addr[i].key != reserved::kEmpty)
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{
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uint32_t value = B.addr[i].value;
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if (value != reserved::kEmpty)
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{
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// printf("INSERTING key %d value %d\n", pHashTableB[threadid].key, value);
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A.insert(B.addr[i]);
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}
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}
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}
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}
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class hashtable_fusion_t : public stream_reduction_operator<hashtable>
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{
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void op(const hashtable& in, hashtable& inout, const exec_place& e, cudaStream_t s) override
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{
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if (e.affine_data_place().is_host())
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{
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cuda_safe_call(cudaStreamSynchronize(s)); // TODO use a callback
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cpu_merge_hashtable(inout, in);
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}
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else
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{
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gpu_merge_hashtable<<<32, 32, 0, s>>>(inout, in);
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}
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}
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void init_op(hashtable& /*unused*/, const exec_place& /*unused*/, cudaStream_t /*unused*/) override
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{
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// This init operator is a no-op because hashtables are already
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// initialized as empty tables
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}
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};
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// A kernel to fill the hashtable with some fictitious values
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__global__ void fill_table(size_t dev_id, size_t cnt, hashtable h)
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{
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unsigned int threadid = blockIdx.x * blockDim.x + threadIdx.x;
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unsigned int nthreads = blockDim.x * gridDim.x;
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for (unsigned int i = threadid; i < cnt; i += nthreads)
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{
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uint32_t key = dev_id * 1000 + i;
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uint32_t value = 2 * i;
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reserved::KeyValue kvs(key, value);
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h.insert(kvs);
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}
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}
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int main()
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{
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stream_ctx ctx;
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// Explicit capacity of 2048 entries
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hashtable refh(2048);
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auto h_handle = ctx.logical_data(refh);
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auto fusion_op = std::make_shared<hashtable_fusion_t>();
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for (size_t dev_id = 0; dev_id < 4; dev_id++)
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{
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ctx.task(h_handle.relaxed(fusion_op))->*[&](auto stream, auto h) {
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EXPECT(h.get_capacity() == 2048);
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fill_table<<<32, 32, 0, stream>>>(dev_id, 10, h);
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};
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}
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ctx.host_launch(h_handle.read())->*[&](auto h) {
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// Check that the table contains all values
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for (size_t dev_id = 0; dev_id < 4; dev_id++)
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{
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for (unsigned i = 0; i < 10; i++)
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{
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uint32_t key = static_cast<uint32_t>(dev_id * 1000 + i);
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uint32_t value = 2 * i;
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EXPECT(h.get(key) == value);
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}
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}
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};
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ctx.finalize();
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}
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