Learning and practice of high performance computing

Practice

cux -- An experimental framework for performance analysis and optimization of CUDA kernel functions.

https://github.com/cjmcv/hpc/tree/master/0-frameworks/cux

tag: cuda / simd / openmp.

hcs -- A heterogeneous computing system for multi-task scheduling optimization.

https://github.com/cjmcv/hpc/tree/master/0-frameworks/hcs

tag: std::thread / cuda.

vky -- A Vulkan-based computing framework.

https://github.com/cjmcv/hpc/tree/master/0-frameworks/vky

tag: vulkan.

mrpc -- Mini-RPC, based on asio.

https://github.com/cjmcv/hpc/tree/master/0-frameworks/mrpc

tag: distributed computing.

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Learning

Distributed computing

mpi/mpi4py

• alg_matrix_multiply ： gemm: C = A * B.
• base_broadcast_scatter_gather ： Record the basic usage of Bcast, Scatter, Gather and Allgather.
• base_group ： Group communication.
• base_hello_world ： Environment Management Routines.
• base_reduce_alltoall_scan ： Record the basic usage of Reduce, Allreduce, Alltoall, Scan and Exscan.
• base_send_recv ： Record the basic usage of MPI_Send/MPI_Recv and MPI_ISend/MPI_IRecv.
• base_type_contiguous ： Send and receive custom types of data by using MPI_Type_contiguous.
• base_type_struct ： Send and receive custom types of data by using MPI_Type_struct.
• util_bandwidth_test ： Test bandwidth by point-to-point communications.
• py_base_broadcast_scatter_gather ： Record the basic usage of Bcast, Scatter, Gather and Allgather.
• py_base_reduce_scan ： Record the basic usage of Reduce and Scan.
• py_base_send_recv ： Record the basic usage of Send and Recv.

Heterogeneous computing

cuda

• cuda_util ： Utility functions.
• alg_histogram ： histogram, mainly introduce atomicAdd.
• alg_matrix_multiply ： gemm: C = A * B.
• alg_vector_add ： Vector addition: C = A + B.
• alg_vector_dot_product ： Vector dot product: h_result = SUM(A * B).
• alg_vector_scan ： Scan. Prefix Sum.
• base_aligned_memory_access ： An experiment on aligned memory access.
• base_bank_conflict ： An experiment on Bank Conflict in Shared Memory.
• base_coalesced_memory_access ： An experiment on coalesced memory access.
• base_float2half ： Record the basic usage of float2half.
• base_graph ： Record the basic usage of cuda graph.
• base_hyperQ ： Demonstrate how HyperQ allows supporting devices to avoid false dependencies between kernels in different streams.
• base_kernel_layout ： Record the basic execution configuration of kernel.
• base_occupancy ： Record the basic usage of cudaOccupancyMaxPotentialBlockSize.
• base_texture ： Record the basic usage of Texture Memory.
• base_unified_memory ： A simple task consumer using threads and streams with all data in Unified Memory.
• base_zero_copy ： Record the basic usage of Zero Copy.
• cub_block_reduce ： Simple demonstration of cub::BlockReduce.
• cub_block_scan ： Simple demonstration of cub::BlockScan.
• cub_device_reduce ： Simple demonstration of DeviceScan::Sum.
• cub_device_scan ： Simple demonstration of DeviceScan::ExclusiveSum.
• cub_warp_reduce ： Simple demonstration of cub::WarpReduce.
• cub_warp_scan ： Simple demonstration of cub::WarpScan.
• cublas_gemm_float16 ： gemm: C = A * B. Use cublas with half-precision.
• thrust_iterators ： Record the basic usage of Iterators in Thrust.
• thrust_sort ： Sort arrays with Thrust.
• thrust_transformations ： Some of the parallel vector operations in Thrust.
• thrust_vector ： Record the basic usage of Vector in Thrust.

vulkan

• vky

opencl

• ocl_util ： Utility functions.
• alg_dot_product ： Vector dot product, h_result = SUM(A * B).
• alg_vector_add ： Vector addition: C = A + B.
• base_platform_info ： Query OpenCL platform information.

Thread

std

• alg_quick_sort： Quick sort using std::thread.
• alg_vector_dot_product： Vector dot product: h_result = SUM(A * B). Record the basic usage of std::tread and std::sync.
• base_async： Record the basic usage of std::async.
• util_blocking_queue： Blocking queue. Mainly implemented by thread, queue and condition_variable.
• util_internal_thread： Internal Thread. Mainly implemented by std::thread.
• util_thread_pool： Thread Pool. Mainly implemented by thread, queue, future and condition_variable.

openmp

• alg_matrix_multiply ： gemm: C = A * B.
• alg_pi_calculate ： Calculate PI using parallel, for and reduction.
• base_flush ： Records the basic usage of flush.
• base_mutex ： Mutex operation in openmp, including critical, atomic, lock.
• base_parallel_for ： Parallel and For.
• base_schedule ： Records the basic usage of schedule.
• base_sections_single ： Records the basic usage of Sections and Single.
• base_synchronous ： Synchronous operation in openmp, including barrier, ordered and master.

tbb

• base_allocator ： The basic use of allocator.
• base_atomic ： The basic use of atomic.
• base_concurrent_hash_map ： The basic use of concurrent_hash_map.
• base_concurrent_queue ： The basic use of concurrent queue.
• base_mutex ： The basic use of mutex in tbb.
• base_parallel_for ： The basic use of parallel_for.
• base_parallel_reduce ： The basic use of parallel_reduce.
• base_parallel_scan ： The basic use of parallel_scan.
• base_parallel_sort ： The basic use of base_parallel_sort.
• base_task_scheduler ： The basic use of base_task_scheduler.
• count_strings ： Count strings. Use the concurrent_hash_map.

Coroutines

libco

asyncio

• base_future： Record the basic usage of future.
• base_gather： Use gather to execute tasks in parallel.
• base_hello_world： Hello world. Record the basic usage of async, await and loop.
• base_loop_chain： Executes nested coroutines.

SIMD

sse/avx

• matrix_multiply ： Matrix Multiplication.
• matrix_transpose ： Matrix Transpose.
• vector_dot_product ： Vector dot product: result = SUM(A * B).
• vector_scan ： Scan. Prefix Sum.

neon

• matrix_multiply ： Matrix Multiplication.
• matrix_transpose ： Matrix Transpose.

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