Running large language models on a single GPU for throughput-oriented scenarios.

A thread-safe queue faster and more resource efficient than golang's native channels

Time series monitoring and alerting platform.

Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)

[NeurIPS'23] H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models.

quacc is a flexible platform for computational materials science and quantum chemistry that is built for the big data era.

atomate2 is a library of computational materials science workflows

ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference

extremly light uart library for AVR 8 bit microcontrollers

A low-latency LRU approximation cache in C++ using CLOCK second-chance algorithm. Multi level cache too. Up to 2.5 billion lookups per second.

An Intuitive, Lightweight, High Performance Full Stack Java Web Framework.

A reactive driver for Aeron transport (https://github.com/real-logic/aeron)

A Simple Way of Creating Job Workflows in Go running in Processes, Containers, Tasks, Pods, or Jobs

Full automation of relative protein-ligand binding free energy calculations in GROMACS

🔬🖥 Automated Blood Vasculature Analysis of 3D Light-Sheet Image Volumes

Window-Based Hybrid CPU/GPU Stream Processing Engine

A low-latency, high-throughput order matching system implementation.

The Zavolab Automated RNA-seq Pipeline

High-throughput molecular docking with multiple targets and ligands using Vina series engines

MatID is a python package for identifying and analyzing atomistic systems based on their structure. MatID is designed to help researchers in the automated analysis and labeling of atomistic datasets.