Artifact for "Triple Overlap in Pipelined Krylov Methods on Multi-GPU Systems"

Artifacts for "Triple Overlap in Pipelined Krylov Methods on Multi-GPU Systems"=============================================== This directory contains computational artifacts related to our SC'26workshop paper. The paper describes computational experiments that were conducted toevaluate the performance of pipelined BiCGStab and pipelinedpreconditioned CG solvers in aCG, using NCCL for communication, andcompares them with the corresponding solvers in PETSc on two GPUclusters: MareNostrum 5 (4 NVIDIA H100 GPUs per node) and Leonardo (4NVIDIA A100 GPUs per node). History:-------- v1 (2026-09-25): Initial version Artifacts:---------- The computational artifacts are located in several subdirectories: * 'src/aCG/' contains the source code for aCG (https://github.com/ParCoreLab/aCG, commit 5ff8adb), which implements the multi-GPU solvers that are used for the performance benchmarks presented in the paper. * 'data/' contains the partitions of the input matrices. From the SuiteSparse Collection (Davis and Hu, 2011), six matrices were selected: audikw_1, Bump_2911, Flan_1565, Geo_1438, Queen_4147 and Serena. Row partitions computed with METIS (Karypis and Kumar, 1998) are provided for 4, 8, 16, 32 and 64 parts ('suitesparse/partitions/'). The matrices themselves are not included; scripts/download-matrices.sh downloads them in Matrix Market format from the SuiteSparse Matrix Collection into 'suitesparse/mtx/' and decompresses the partitions: scripts/download-matrices.sh The checksums of all files are listed in data/SHA256SUMS; the download script verifies the SuiteSparse files against them. * 'scripts/' contains scripts for building aCG and job scripts for submitting jobs on two clusters: MareNostrum 5 ('marenostrum5/') and Leonardo ('leonardo/'). These scripts carry out performance measurements for the pipelined solvers in aCG and PETSc. The README in the subdirectory for each cluster describes how to configure the environment, build aCG, run the jobs and collect the results. 'collect/' contains a script that turns the job output into a CSV file with solver times, and 'figures/' contains the scripts that produce the figures of the paper from that file. References---------- Davis, T. A. and Y. Hu. 2011. “The University of Florida Sparse Matrix Collection”.ACM Transactions on Mathematical Software 38, 1, Article 1 (December 2011), 25 pages.DOI: https://doi.org/10.1145/2049662.2049663 Karypis, G., and V. Kumar. 1998. “A fast and high quality multilevel scheme for partitioning irregular graphs”.SIAM Journal on scientific Computing 20, 1, pp. 359–392.DOI: https://doi.org/10.1137/S1064827595287997