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
94 lines
2.9 KiB
Plaintext
94 lines
2.9 KiB
Plaintext
// SPDX-FileCopyrightText: Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
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// SPDX-License-Identifier: BSD-3-Clause
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// %RANGE% TUNE_BIF_BIAS bif -16:16:4
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// %RANGE% TUNE_ALGORITHM alg 0:4:1
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// %RANGE% TUNE_THREADS tpb 128:1024:128
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// for TUNE_ALGORITHM == 1 (vectorized), this is the number of vectors per thread, which is similar in spirit
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// %RANGE% TUNE_UNROLL_FACTOR unrl 1:4:1
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// those parameters only apply if TUNE_ALGORITHM == 0 (prefetch)
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// %RANGE% TUNE_PREFETCH_MULT pref 1:3:1
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// those parameters only apply if TUNE_ALGORITHM == 1 (vectorized)
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// %RANGE% TUNE_VEC_SIZE_POW2 vsp2 1:6:1
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#if !TUNE_BASE && TUNE_ALGORITHM != 0 && (TUNE_PREFETCH_MULT != 1)
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# error "Non-prefetch algorithms require prefetch multiple to be 1 since they ignore the parameters"
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#endif // !TUNE_BASE && TUNE_ALGORITHM != 0 && (TUNE_PREFETCH_MULT != 1)
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#if !TUNE_BASE && TUNE_ALGORITHM != 1 && (TUNE_VEC_SIZE_POW2 != 1)
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# error "Non-vectorized algorithms require vector size to be 1 since they ignore the parameters"
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#endif // !TUNE_BASE && TUNE_ALGORITHM != 1 && (TUNE_VEC_SIZE_POW2 != 1)
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#include "common.h"
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// This benchmark uses a LOT of registers and is compute intensive.
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template <int N>
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struct heavy_functor
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{
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// we need to use an unsigned type so overflow in arithmetic wraps around
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__device__ std::uint32_t operator()(std::uint32_t data) const
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{
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std::uint32_t reg[N];
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reg[0] = data;
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for (int i = 1; i < N; ++i)
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{
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reg[i] = reg[i - 1] * reg[i - 1] + 1;
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}
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for (int i = 0; i < N; ++i)
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{
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reg[i] = (reg[i] * reg[i]) % 19;
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}
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for (int i = 0; i < N; ++i)
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{
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reg[i] = reg[N - i - 1] * reg[i];
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}
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std::uint32_t x = 0;
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for (int i = 0; i < N; ++i)
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{
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x += reg[i];
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}
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return x;
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}
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};
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template <typename Heaviness>
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static void heavy(nvbench::state& state, nvbench::type_list<Heaviness>)
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try
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{
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using value_t = std::uint32_t;
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const auto n = state.get_int64("Elements{io}");
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thrust::device_vector<value_t> in = generate(n);
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thrust::device_vector<value_t> out(n);
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state.add_element_count(n);
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state.add_global_memory_reads<value_t>(n);
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state.add_global_memory_writes<value_t>(n);
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bench_transform(state, cuda::std::tuple{in.begin()}, out.begin(), n, heavy_functor<Heaviness::value>{});
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}
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catch (const std::bad_alloc&)
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{
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state.skip("Skipping: out of memory.");
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}
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using ::cuda::std::integral_constant;
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#ifdef TUNE_Heaviness
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using heaviness = nvbench::type_list<TUNE_Heaviness>; // expands to "integral_constant<int, ...>"
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#else
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using heaviness =
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nvbench::type_list<integral_constant<int, 32>,
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integral_constant<int, 64>,
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integral_constant<int, 128>,
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integral_constant<int, 256>>;
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#endif
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NVBENCH_BENCH_TYPES(heavy, NVBENCH_TYPE_AXES(heaviness))
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.set_name("heavy")
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.set_type_axes_names({"Heaviness{ct}"})
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.add_int64_power_of_two_axis("Elements{io}", nvbench::range(16, 32, 4));
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