high performance computing on graphics processing units: hgpu.org

Jiajun Huang, Sheng Di, Xiaodong Yu, Yujia Zhai, Jinyang Liu, Yafan Huang, Ken Raffenetti, Hui Zhou, Kai Zhao, Zizhong Chen, Franck Cappello, Yanfei Guo, Rajeev Thakur

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Tags: Compression, Computer science, GPU cluster, MPI, nVidia, nVidia A100

August 13, 2023 by hgpu

Isolated Scheduling for Distributed Training Tasks in GPU Clusters

Xinchi Han, Weihao Jiang, Peirui Cao, Qinwei Yang, Yunzhuo Liu, Shuyao Qi, Shengkai Lin, Shizhen Zhao

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Tags: Computer science, GPU cluster, Machine learning, Neural networks, nVidia, nVidia V100, Package

August 13, 2023 by hgpu

Bandicoot: C++ Library for GPU Linear Algebra and Scientific Computing

Ryan R. Curtin, Marcus Edel, Conrad Sanderson

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Programming techniques

July 30, 2023 by hgpu

Fast Knowledge Graph Completion using Graphics Processing Units

Chun-Hee Lee, Dong-oh Kang, Hwa Jeon Song

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Tags: AI, Algorithms, Computer science, CUDA, Databases, Graph theory, nVidia, nVidia A100

July 30, 2023 by hgpu

A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

Pietro Incardona, Aryaman Gupta, Serhii Yaskovets, Ivo F. Sbalzarini

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Tags: AMD RX Vega 64, ATI, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, OpenACC, OpenCL, OpenMP, Package, Performance, performance portability, SYCL

July 30, 2023 by hgpu

Creating a Dataset Supporting Translation Between OpenMP Fortran and C++ Code

Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao

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Tags: Computer science, CUDA, Fortran, Machine learning, nVidia, nVidia RTX 6000 Ada, OpenMP, Package

July 24, 2023 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

Increased reliability on Intel GPUs via software diverse redundancy

Quantifying OpenMP: Statistical Insights into Usage and Adoption

A Model Extraction Attack on Deep Neural Networks Running on GPUs

SYnergy: Fine-grained Energy-Efficient Heterogeneous Computing for Scalable Energy Saving

Static and Dynamic Analyses for Efficient GPU Execution

gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters

Isolated Scheduling for Distributed Training Tasks in GPU Clusters

Bandicoot: C++ Library for GPU Linear Algebra and Scientific Computing

Fast Knowledge Graph Completion using Graphics Processing Units

A portable C++ library for memory and compute abstraction on multi-core CPUs and GPUs

Creating a Dataset Supporting Translation Between OpenMP Fortran and C++ Code

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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