From b5e3e19c2845f22fb338f4a4bc4b1ccee861d026 Mon Sep 17 00:00:00 2001 From: Scott Beamer Date: Sat, 11 May 2024 11:11:11 -0700 Subject: [PATCH] [docs] fix typos in README --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 72be2dd..4e3183f 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ Kernels Included ---------------- + Breadth-First Search (BFS) - direction optimizing + Single-Source Shortest Paths (SSSP) - delta stepping -+ PageRank (PR) - iterative method in pull direction, Gauss-Seidel or Jacobi ++ PageRank (PR) - iterative method in pull direction, Gauss-Seidel & Jacobi + Connected Components (CC) - Afforest & Shiloach-Vishkin + Betweenness Centrality (BC) - Brandes + Triangle Counting (TC) - order invariant with possible degree relabelling @@ -90,7 +90,7 @@ Please cite this code by the benchmark specification: To Learn More ------------- -The [specification](http://arxiv.org/abs/1508.03619) (above) provides the most detailed description of the benchmark and the reference implementation. The benchmark kernels were selected by a thorough methodology including an extensive literature search [2] and a detailed workload characterization [3]. In 2020, the leading shared-memory graph frameworks competed accoring to the GAP Benchmark specifications [1]. +The [specification](http://arxiv.org/abs/1508.03619) (above) provides the most detailed description of the benchmark and the reference implementation. The benchmark kernels were selected by a thorough methodology including an extensive literature search [2] and a detailed workload characterization [3]. In 2020, the leading shared-memory graph frameworks competed according to the GAP Benchmark specifications [1]. 1. Ariful Azad, Mohsen Mahmoudi Aznaveh, Scott Beamer, et al. [*Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite*](https://ieeexplore.ieee.org/document/9251247). International Symposium on Workload Characterization (IISWC), October 2020.