From 965b604e244979c8971b2d954ed2585eb912302e Mon Sep 17 00:00:00 2001
From: CharlesCNorton <135471798+CharlesCNorton@users.noreply.github.com>
Date: Wed, 7 Aug 2024 16:49:46 -0400
Subject: [PATCH] Fix grammatical errors in documentation

1. Changed "Its worth noting" to "It's worth noting" to correctly use the contraction for "it is."

2. Corrected "comprises of" to "comprises" to properly reflect the intended meaning.
---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 6ffefb10..2a04766e 100644
--- a/README.md
+++ b/README.md
@@ -4,9 +4,9 @@ PARAM Benchmarks is a repository of communication and compute micro-benchmarks a
 
 PARAM complements two broad categories of commonly used benchmarks:
 1. C++ based stand-alone compute and communication benchmarks using cuDNN, MKL, NCCL, MPI libraries - e.g., NCCL tests (https://github.com/NVIDIA/nccl-tests), OSU MPI benchmarks (https://mvapich.cse.ohio-state.edu/benchmarks/), and DeepBench (https://github.com/baidu-research/DeepBench).
-2. Application benchmarks such as Deep Learning Recommendation Model (DLRM) and the broader MLPerf benchmarks. Its worth noting that while MLPerf is the de-facto industry standard for benchmarking ML applications we hope to compliment this effort with broader workloads that are of more interest to Facebook with more in-depth analysis of each within this branch of Application benchmarks.
+2. Application benchmarks such as Deep Learning Recommendation Model (DLRM) and the broader MLPerf benchmarks. It's worth noting that while MLPerf is the de-facto industry standard for benchmarking ML applications we hope to compliment this effort with broader workloads that are of more interest to Facebook with more in-depth analysis of each within this branch of Application benchmarks.
 
-Our initial release of PARAM benchmarks focuses on AI training and comprises of:
+Our initial release of PARAM benchmarks focuses on AI training and comprises:
 1. Communication: PyTorch based collective benchmarks across arbitrary message sizes, effectiveness of compute-communication overlap, and DLRM communication patterns in fwd/bwd pass
 2. Compute: PyTorch based GEMM, embedding lookup, and linear layer
 3. DLRM: tracks the `ext_dist` branch of DRLM benchmark use Facebook's DLRM benchmark (https://github.com/facebookresearch/dlrm). In short, PARAM fully relies on DLRM benchmark for end-to-end workload evaluation; with additional extensions as required for scale-out AI training platforms.