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# Contents of this release Examples: multi-gpu example #381 Examples: updates example compares Radix2 and MixedRadix NTTs #383 Feat: add vector operations bindings to Rust #384 Examples: update examples with new vec ops #388 Feat: Grumpkin curve implementation #379 Feat: mixed-radix NTT fast twiddles mode #382 Docs: Update README.md #385 #387 README: Update Hall of Fame section #394 Examples: add rust poseidon example #392 Feat: GoLang bindings for v1.x #386
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| cmake_minimum_required(VERSION 3.18) | ||
| set(CMAKE_CXX_STANDARD 17) | ||
| set(CMAKE_CUDA_STANDARD 17) | ||
| set(CMAKE_CUDA_STANDARD_REQUIRED TRUE) | ||
| set(CMAKE_CXX_STANDARD_REQUIRED TRUE) | ||
| if (${CMAKE_VERSION} VERSION_LESS "3.24.0") | ||
| set(CMAKE_CUDA_ARCHITECTURES ${CUDA_ARCH}) | ||
| else() | ||
| set(CMAKE_CUDA_ARCHITECTURES native) # on 3.24+, on earlier it is ignored, and the target is not passed | ||
| endif () | ||
| project(icicle LANGUAGES CUDA CXX) | ||
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| set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --expt-relaxed-constexpr") | ||
| set(CMAKE_CUDA_FLAGS_RELEASE "") | ||
| set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -g -G -O0") | ||
| # change the path to your Icicle location | ||
| include_directories("../../../icicle") | ||
| add_executable( | ||
| example | ||
| example.cu | ||
| ) | ||
| find_library(NVML_LIBRARY nvidia-ml PATHS /usr/local/cuda/targets/x86_64-linux/lib/stubs/ ) | ||
| target_link_libraries(example ${NVML_LIBRARY}) | ||
| set_target_properties(example PROPERTIES CUDA_SEPARABLE_COMPILATION ON) | ||
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| # Icicle example: using multiple GPU to hash large dataset | ||
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| ## Best-Practices | ||
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| This example builds on [single GPU Poseidon example](../poseidon/README.md) so we recommend to run it first. | ||
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| ## Key-Takeaway | ||
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| Use `device_context::DeviceContext` variable to select GPU to use. | ||
| Use C++ threads to compute `Icicle` primitives on different GPUs in parallel. | ||
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| ## Concise Usage Explanation | ||
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| 1. Include c++ threads | ||
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| ```c++ | ||
| #include <thread> | ||
| ``` | ||
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| 2. Define a __thread function__. Importantly, device context `ctx` will hold the GPU id. | ||
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| ```c++ | ||
| void threadPoseidon(device_context::DeviceContext ctx, ...) {...} | ||
| ``` | ||
| 3. Initialize device contexts for different GPUs | ||
| ```c++ | ||
| device_context::DeviceContext ctx0 = device_context::get_default_device_context(); | ||
| ctx0.device_id=0; | ||
| device_context::DeviceContext ctx1 = device_context::get_default_device_context(); | ||
| ctx1.device_id=1; | ||
| ``` | ||
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| 4. Finally, spawn the threads and wait for their completion | ||
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| ```c++ | ||
| std::thread thread0(threadPoseidon, ctx0, ...); | ||
| std::thread thread1(threadPoseidon, ctx1, ...); | ||
| thread0.join(); | ||
| thread1.join(); | ||
| ``` | ||
| ## What's in the example | ||
| This is a **toy** example executing the first step of the Filecoin's Pre-Commit 2 phase: compute $2^{30}$ Poseison hashes for each column of $11 \times 2^{30}$ matrix. | ||
| 1. Define the size of the example: $2^{30}$ won't fit on a typical machine, so we partition the problem into `nof_partitions` | ||
| 2. Hash two partitions in parallel on two GPUs | ||
| 3. Hash two partitions in series on one GPU | ||
| 4. Compare execution times | ||
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| #!/bin/bash | ||
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| # Exit immediately on error | ||
| set -e | ||
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| rm -rf build | ||
| mkdir -p build | ||
| cmake -S . -B build | ||
| cmake --build build |
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| #include <iostream> | ||
| #include <thread> | ||
| #include <chrono> | ||
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| #include <nvml.h> | ||
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| // select the curve | ||
| #define CURVE_ID 2 | ||
| #include "appUtils/poseidon/poseidon.cu" | ||
| #include "utils/error_handler.cuh" | ||
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| using namespace poseidon; | ||
| using namespace curve_config; | ||
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| void checkCudaError(cudaError_t error) { | ||
| if (error != cudaSuccess) { | ||
| std::cerr << "CUDA error: " << cudaGetErrorString(error) << std::endl; | ||
| // Handle the error, e.g., exit the program or throw an exception. | ||
| } | ||
| } | ||
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| // these global constants go into template calls | ||
| const int size_col = 11; | ||
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| // this function executes the Poseidon thread | ||
| void threadPoseidon(device_context::DeviceContext ctx, unsigned size_partition, scalar_t * layers, scalar_t * column_hashes, PoseidonConstants<scalar_t> * constants) { | ||
| cudaError_t err_result = CHK_STICKY(cudaSetDevice(ctx.device_id)); | ||
| if (err_result != cudaSuccess) { | ||
| std::cerr << "CUDA error: " << cudaGetErrorString(err_result) << std::endl; | ||
| return; | ||
| } | ||
| // CHK_IF_RETURN(); I can't use it in a standard thread function | ||
| PoseidonConfig column_config = { | ||
| ctx, // ctx | ||
| false, // are_inputes_on_device | ||
| false, // are_outputs_on_device | ||
| false, // input_is_a_state | ||
| false, // aligned | ||
| false, // loop_state | ||
| false, // is_async | ||
| }; | ||
| cudaError_t err = poseidon_hash<scalar_t, size_col+1>(layers, column_hashes, (size_t) size_partition, *constants, column_config); | ||
| checkCudaError(err); | ||
| } | ||
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| using FpMilliseconds = std::chrono::duration<float, std::chrono::milliseconds::period>; | ||
| #define START_TIMER(timer) auto timer##_start = std::chrono::high_resolution_clock::now(); | ||
| #define END_TIMER(timer, msg) printf("%s: %.0f ms\n", msg, FpMilliseconds(std::chrono::high_resolution_clock::now() - timer##_start).count()); | ||
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| #define CHECK_ALLOC(ptr) if ((ptr) == nullptr) { \ | ||
| std::cerr << "Memory allocation for '" #ptr "' failed." << std::endl; \ | ||
| exit(EXIT_FAILURE); \ | ||
| } | ||
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| int main() { | ||
| const unsigned size_row = (1<<30); | ||
| const unsigned nof_partitions = 64; | ||
| const unsigned size_partition = size_row / nof_partitions; | ||
| // layers is allocated only for one partition, need to reuse for different partitions | ||
| const uint32_t size_layers = size_col * size_partition; | ||
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| nvmlInit(); | ||
| unsigned int deviceCount; | ||
| nvmlDeviceGetCount(&deviceCount); | ||
| std::cout << "Available GPUs: " << deviceCount << std::endl; | ||
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| for (unsigned int i = 0; i < deviceCount; ++i) { | ||
| nvmlDevice_t device; | ||
| nvmlMemory_t memory; | ||
| char name[NVML_DEVICE_NAME_BUFFER_SIZE]; | ||
| nvmlDeviceGetHandleByIndex(i, &device); | ||
| nvmlDeviceGetName(device, name, NVML_DEVICE_NAME_BUFFER_SIZE); | ||
| nvmlDeviceGetMemoryInfo(device, &memory); | ||
| std::cout << "Device ID: " << i << ", Type: " << name << ", Memory Total/Free (MiB) " << memory.total/1024/1024 << "/" << memory.free/1024/1024 << std::endl; | ||
| } | ||
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| const unsigned memory_partition = sizeof(scalar_t)*(size_col+1)*size_partition/1024/1024; | ||
| std::cout << "Required Memory (MiB) " << memory_partition << std::endl; | ||
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| //=============================================================================== | ||
| // Key: multiple devices are supported by device context | ||
| //=============================================================================== | ||
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| device_context::DeviceContext ctx0 = device_context::get_default_device_context(); | ||
| ctx0.device_id=0; | ||
| device_context::DeviceContext ctx1 = device_context::get_default_device_context(); | ||
| ctx1.device_id=1; | ||
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| std::cout << "Allocate and initialize the memory for layers and hashes" << std::endl; | ||
| scalar_t* layers0 = static_cast<scalar_t*>(malloc(size_layers * sizeof(scalar_t))); | ||
| CHECK_ALLOC(layers0); | ||
| scalar_t s = scalar_t::zero(); | ||
| for (unsigned i = 0; i < size_col*size_partition ; i++) { | ||
| layers0[i] = s; | ||
| s = s + scalar_t::one(); | ||
| } | ||
| scalar_t* layers1 = static_cast<scalar_t*>(malloc(size_layers * sizeof(scalar_t))); | ||
| CHECK_ALLOC(layers1); | ||
| s = scalar_t::zero() + scalar_t::one(); | ||
| for (unsigned i = 0; i < size_col*size_partition ; i++) { | ||
| layers1[i] = s; | ||
| s = s + scalar_t::one(); | ||
| } | ||
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| scalar_t* column_hash0 = static_cast<scalar_t*>(malloc(size_partition * sizeof(scalar_t))); | ||
| CHECK_ALLOC(column_hash0); | ||
| scalar_t* column_hash1 = static_cast<scalar_t*>(malloc(size_partition * sizeof(scalar_t))); | ||
| CHECK_ALLOC(column_hash1); | ||
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| PoseidonConstants<scalar_t> column_constants0, column_constants1; | ||
| init_optimized_poseidon_constants<scalar_t>(size_col, ctx0, &column_constants0); | ||
| cudaError_t err_result = CHK_STICKY(cudaSetDevice(ctx1.device_id)); | ||
| if (err_result != cudaSuccess) { | ||
| std::cerr << "CUDA error: " << cudaGetErrorString(err_result) << std::endl; | ||
| return; | ||
| } | ||
| init_optimized_poseidon_constants<scalar_t>(size_col, ctx1, &column_constants1); | ||
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| std::cout << "Parallel execution of Poseidon threads" << std::endl; | ||
| START_TIMER(parallel); | ||
| std::thread thread0(threadPoseidon, ctx0, size_partition, layers0, column_hash0, &column_constants0); | ||
| std::thread thread1(threadPoseidon, ctx1, size_partition, layers1, column_hash1, &column_constants1); | ||
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| // Wait for the threads to finish | ||
| thread0.join(); | ||
| thread1.join(); | ||
| END_TIMER(parallel,"2 GPUs"); | ||
| std::cout << "Output Data from Thread 0: "; | ||
| std::cout << column_hash0[0] << std::endl; | ||
| std::cout << "Output Data from Thread 1: "; | ||
| std::cout << column_hash1[0] << std::endl; | ||
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| std::cout << "Sequential execution of Poseidon threads" << std::endl; | ||
| START_TIMER(sequential); | ||
| std::thread thread2(threadPoseidon, ctx0, size_partition, layers0, column_hash0, &column_constants0); | ||
| thread2.join(); | ||
| std::thread thread3(threadPoseidon, ctx0, size_partition, layers1, column_hash1, &column_constants0); | ||
| thread3.join(); | ||
| END_TIMER(sequential,"1 GPU"); | ||
| std::cout << "Output Data from Thread 2: "; | ||
| std::cout << column_hash0[0] << std::endl; | ||
| std::cout << "Output Data from Thread 3: "; | ||
| std::cout << column_hash1[0] << std::endl; | ||
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| nvmlShutdown(); | ||
| return 0; | ||
| } |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,2 @@ | ||
| #!/bin/bash | ||
| ./build/example |
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