This project implements a Sparse Matrix-Vector Multiplication (SpMV) calculator using CSR and COO formats and optimizations on both CPU and GPU. The program loads a sparse matrix and a vector from a file, and calculates the matrix-vector product.

• The GPU part is implemented using Apple Metal Shading Language
• The CPU part is implemented using OpenMP

This program can only run on Apple Chip Laptop, such as M1, M1 Pro, M1 Max, M2, M2 Pro, M2 Max.

# Install llvm version of clang to support OpenMP
brew install llvm
# Install OpenMP
brew install libomp
# Install GSL
brew install gsl
# Install cmake
brew install cmake
# Install XCode Command Line Tools to support Metal Compiler
xcode-select --install

The program error tips are very friendly, so you can just run the program and see the tips.

cmake .
make && make kernel
./spmv xxx.mtx

• spmv.metal

  This is the Metal kernel using Metal Shading Language. It provides three kernel functions to perform Sparse Matrix-Vector Multiplication (SpMV) using different formats and optimizations on GPU.

  • spmv_csr

    This kernel function calculates the SpMV product using the CSR format. It takes in row pointers, column indices, values, input vector x, and an output vector result. The kernel processes each row of the sparse matrix in parallel.

  • spmv_coo

    Optimization 1 for spmv_csr. This kernel function calculates the SpMV product using the COO format. It processes the non-zero elements of the sparse matrix in parallel. It takes row indices, column indices, values, input vector x, and an output vector result (with atomic float type).

  • spmv_csr_loop_unrolling

    Optimization 2 for spmv_csr. This kernel function applies loop unrolling optimization. The loop unrolling is performed with a step of 4 to potentially improve performance.

• spmv_calculator.h / spmv_calculator.cpp

  The SpmvCalculator is responsible for performing SpMV calculations using different approaches:

  • GPU with CSR format
  • GPU with CSR format and loop unrolling optimization
  • GPU with COO format
  • CPU with serial processing
  • CPU with OpenMP parallel processing

  It also provides a method for verifying the results.

• logger.h / logger.cpp

  The Logger provides static methods for logging messages of various levels: info, error, debug, and warn. It also includes a method, time, for measuring and logging the time taken by specific operations.

• The Apple GPU with Metal Shading Language (MSL) can run up to 1024 threads. Technically, it can achieve a 1000x speedup, confirmed by running all the computations inside a single kernel; the code logic was omitted.
• GPU MSL kernel optimization is not as efficient as CPU optimization, such as shading overhead and kernel compiler optimization. As a result, it experiences performance loss.
• The basic algorithm uses CSR and assigns each row to a thread, which then serially loops through the non-zero values in the row to compute the final results.
• Optimization 1: Employ COO format instead of CSR, distributing all non-zero values to each GPU thread and atomically summing the results. While this algorithm should outperform the CSR format, atomic reduction overhead and memory cache issues prevent significant improvement.
• Optimization 2: Since Metal GPU compiler optimization is less mature than C++, loop unrolling can lead to significant speedup due to SIMD optimization.

Conclusion:

• CPU OpenMP with 4 threads achieves a 4x speedup compared to the CPU Serial Version (TEST-1 vs TEST-2, TEST-6 vs TEST-7).

• GPU COO runs slightly faster than GPU CSR (TEST-3 vs TEST-5, TEST-8 vs TEST-10).

• GPU CSR with loop unrolling optimization achieves a 2x speedup (TEST-3 vs TEST-4, TEST-8 vs TEST-9).

• CPU Serial and OpenMP versions attain a 6x speedup with -O3 compiler optimization (TEST-1 vs TEST-6, TEST-2 vs TEST-7).

• GPU CSR provides a 25x speedup compared to the CPU Serial without compiler optimization and a 4x speedup compared to CPU Serial with -O3 optimization (TEST-1 vs TEST-4, TEST-6 vs TEST-9).

• Comparing the best versions, the GPU still runs faster than the CPU (TEST-7 vs TEST-9).

Running Results:

• Results Table

• CPU applies -O0

• C++ applies -O3

nlpkkt80

af_shell10

StocF-1465