YuhanLiu11

mscclpp

MSCCL++: A GPU-driven communication stack for scalable AI applications

CacheBlend

LMCache

Grace

lm-evaluation-harness

A framework for few-shot evaluation of language models.

LLM-Zoo

LLM Zoo collects information of various open- and close-sourced LLMs

pythia

The hub for EleutherAI's work on interpretability and learning dynamics

SumGPT

Bulk summarization of documents using ChatGPT API

Real-ESRGAN

Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.

TaskMatrix

MadFS

Source code for the FAST '23 paper “MadFS: Per-File Virtualization for Userspace Persistent Memory Filesystems”

shockwave

Code for "Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning" [NSDI '23]

AccMPEG

dds

Server-driven Video Streaming for Deep Learning Inference

meshinsight

MeshInsight: Dissecting Overheads of Service Mesh Sidecars

kvazaar

An open-source HEVC encoder

cancellation-study-osdi

FedScale

FedScale is a scalable and extensible open-source federated learning (FL) platform.

cs759-sp21

CS/ECE/ME/EP 759 (High Performance Computing for Engineering Applications) Course Project: Cautiously Aggressive GPU Space Sharing to Improve Resource Utilization and Job Efficiency

EfficientNet-PyTorch

A PyTorch implementation of EfficientNet

benchmarking

paper_notes

Personal blog + reading notes on system-ish papers

Project_CodeNet

This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX

Facial-Expression-Recognition.Pytorch

A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset

Accordion

Code for reproducing experiments performed for Accoridon

marius

Large scale graph learning on a single machine.

Multitype-Defect-Detection

Multitype Defect Detection with Faster R-CNN

Sundial

Sundial: A distributed OLTP database testbed.

DBx1000

pytorch-lamb

Implementation of https://arxiv.org/abs/1904.00962