junesookang

junesookang's Stars

• microsoft/unilm

  Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

  Language:Python20.2k3071.4k2.5k

• microsoft/graphrag

  A modular graph-based Retrieval-Augmented Generation (RAG) system

  Language:Python19.1k1245161.9k

• state-spaces/mamba

  Mamba SSM architecture

  Language:Python13.2k995481.1k

• NVIDIA/cuda-samples

  Samples for CUDA Developers which demonstrates features in CUDA Toolkit

  Language:C6.4k1232421.8k

• naganandy/graph-based-deep-learning-literature

  links to conference publications in graph-based deep learning

  Language:Jupyter Notebook4.8k25214776

• linkedin/Liger-Kernel

  Efficient Triton Kernels for LLM Training

  Language:Python3.4k3998202

• togethercomputer/MoA

  Together Mixture-Of-Agents (MoA) – 65.1% on AlpacaEval with OSS models

  Language:Python2.6k3518356

• snap-stanford/ogb

  Benchmark datasets, data loaders, and evaluators for graph machine learning

  Language:Python1.9k41298401

• horseee/Awesome-Efficient-LLM

  A curated list for Efficient Large Language Models

  Language:Python1.3k42391

• NVIDIA/gdrcopy

  A fast GPU memory copy library based on NVIDIA GPUDirect RDMA technology

  Language:C++89655187144

• AmberLJC/LLMSys-PaperList

  Large Language Model (LLM) Systems Paper List

  64525024

• forhaoliu/ringattention

  Transformers with Arbitrarily Large Context

  Language:Python62561648

• microsoft/ptgnn

  A PyTorch Graph Neural Network Library

  Language:Python37512640

• feifeibear/long-context-attention

  USP: Unified (a.k.a. Hybrid, 2D) Sequence Parallel Attention for Long Context Transformers Model Training and Inference

  Language:Python35441824

• microsoft/mscclpp

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

  Language:C++250189239

• NVIDIA/gds-nvidia-fs

  NVIDIA GPUDirect Storage Driver

  Language:C202143331

• InfiniTensor/InfiniTensor

  Language:C++16444427

• ZaidQureshi/bam

  Language:Cuda129113732

• microsoft/DeepGNN

  DeepGNN is a framework for training machine learning models on large scale graph data.

  Language:Python122101129

• microsoft/ark

  A GPU-driven system framework for scalable AI applications

  Language:C++109121815

• hgyhungry/ge-spmm

  Language:Cuda10131310

• snu-comparch/InfiniGen

  InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management (OSDI'24)

  Language:Python793017

• IllinoisGraphBenchmark/IGB-Datasets

  Largest realworld open-source graph dataset - Worked done under IBM-Illinois Discovery Accelerator Institute and Amazon Research Awards and in collaboration with NVIDIA Research.

  Language:Python7633211

• Sys-KU/DeepPlan

  Fast and Efficient Model Serving Using Multi-GPUs with Direct-Host-Access (ACM EuroSys '23)

  Language:C++548

• K-Wu/pytorch-direct_dgl

  PyTorch-Direct code on top of PyTorch-1.8.0nightly (e152ca5) for Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture (accepted by PVLDB)

  45375

• jeongminpark417/GIDS

  Language:Python262116

• unist-ssl/IIDP

  Language:Python12

• unist-ssl/JABAS

  "JABAS: Joint Adaptive Batching and Automatic Scaling for DNN Training on Heterogeneous GPUs" (EuroSys '25)

  Language:Python120

• seijimaekawa/empirical-study-of-GNNs

  Language:Jupyter Notebook7101

• HPMLL/HP-SpMM

  Fast SpMM implementation on GPUs for GNN (IPDPS'23)

  Language:C++30