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A Distributed-memory Tridiagonal Solver Based on a Specialised Data Structure Optimised for CPU and GPU Architectures

Semih Akkurt, Sébastien Lemaire, Paul Bartholomew, Sylvain Laizet

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Tags: Computational Physics, Computer science, CUDA, nVidia, nVidia A100, nVidia GH200, nVidia H100, nVidia V100, OpenMP, PDEs

November 24, 2024 by hgpu

SoK: A Systems Perspective on Compound AI Threats and Countermeasures

Sarbartha Banerjee, Prateek Sahu, Mulong Luo, Anjo Vahldiek-Oberwagner, Neeraja J. Yadwadkar, Mohit Tiwari

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Tags: AI, Artificial intelligence, Computer science, LLM, Machine learning, Security

November 24, 2024 by hgpu

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

Bertil Schmidt, Felix Kallenborn, Alexander Wichmann, Alejandro Chacon, Christian Hundt

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Tags: Bioinformatics, Biology, Computer science, CUDA, FPGA, Genomics, nVidia, nVidia A100, nVidia H100, nVidia L4, nVidia L40s, nVidia V100, Package

November 24, 2024 by hgpu

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Giulio Malenza, Valentina Cesare, Marco Edoardo Santimaria, Robert Birke, Alberto Vecchiato, Ugo Becciani, Marco Aldinucci

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Tags: AMD Radeon Instinct MI250X, Astrophysics, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia H100, nVidia V100, OpenMP, Package, Performance, performance portability, SYCL, Tesla T4

November 24, 2024 by hgpu

Edify 3D: Scalable High-Quality 3D Asset Generation

Maciej Bala, Yin Cui, Yifan Ding, Yunhao Ge, Zekun Hao, Jon Hasselgren, Jacob Huffman, Jingyi Jin, J.P. Lewis, Zhaoshuo Li, Chen-Hsuan Lin, Yen-Chen Lin, Tsung-Yi Lin, Ming-Yu Liu, Alice Luo, Qianli Ma, Jacob Munkberg, Stella Shi, Fangyin Wei, Donglai Xiang, Jiashu Xu, Xiaohui Zeng, Qinsheng Zhang

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Tags: 3D Graphics and Realism, Computer science, Computer vision, nVidia

November 24, 2024 by hgpu

Improving Parallel Program Performance Through DSL-Driven Code Generation with LLM Optimizers

Anjiang Wei, Allen Nie, Thiago S. F. X. Teixeira, Rohan Yadav, Wonchan Lee, Ke Wang, Alex Aiken

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Tags: Code generation, Computer science, DSL, LLM, Matrix multiplication, nVidia, Optimization, Tesla P100

November 17, 2024 by hgpu

GPUVM: GPU-driven Unified Virtual Memory

Nurlan Nazaraliyev, Elaheh Sadredini, Nael Abu-Ghazaleh

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Tags: Computer science, CUDA, Memory, nVidia, Operating systems, Performance, Tesla V100

November 17, 2024 by hgpu

Context Parallelism for Scalable Million-Token Inference

Amy (Jie) Yang, Jingyi Yang, Aya Ibrahim, Xinfeng Xie, Bangsheng Tang, Grigory Sizov, Jeremy Reizenstein, Jongsoo Park, Jianyu Huang

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Tags: Artificial intelligence, Benchmarking, Computer science, LLM, nVidia, nVidia H100, Performance

November 17, 2024 by hgpu

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

Lennart Steffen, Reinhard Hinkelmann

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, Intel, Intel Data Center GPU Max 1550, Linear Algebra, nVidia, nVidia A100, nVidia GeForce GTX Titan V, Package, performance portability

November 17, 2024 by hgpu

The Rewriting of DataRaceBench Benchmark for OpenCL Program Validations

Chia-Chen Hsu, Chun-Lin Huang, Chao-Lin Lee, Jenq-Kuen Lee, Pei-Hung Lin

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Tags: Benchmarking, Computer science, LLVM, OpenCL, Performance

November 17, 2024 by hgpu

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

Krishna Teja Chitty-Venkata, Siddhisanket Raskar, Bharat Kale, Farah Ferdaus, Aditya Tanikanti, Ken Raffenetti, Valerie Taylor, Murali Emani, Venkatram Vishwanath

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Tags: AI, AMD Radeon Instinct MI250, AMD Radeon Instinct MI300X, Artificial intelligence, ATI, Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100, nVidia GH200, nVidia H100, OpenCL, Performance

November 10, 2024 by hgpu

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Nathaniel Morgan, Caleb Yenusah, Adrian Diaz, Daniel Dunning,Jacob Moore,Erin Heilman, Calvin Roth, Evan Lieberman, Steven Walton, Sarah Brown, Daniel Holladay, Marko Knezevic, Gavin Whetstone, Zachary Baker, Robert Robey

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Tags: Computer science, CUDA, Fortran, HIP, nVidia, Package, performance portability, Pthreads

November 10, 2024 by hgpu

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

A Distributed-memory Tridiagonal Solver Based on a Specialised Data Structure Optimised for CPU and GPU Architectures

SoK: A Systems Perspective on Compound AI Threats and Countermeasures

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

Edify 3D: Scalable High-Quality 3D Asset Generation

Improving Parallel Program Performance Through DSL-Driven Code Generation with LLM Optimizers

GPUVM: GPU-driven Unified Virtual Memory

Context Parallelism for Scalable Million-Token Inference

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

The Rewriting of DataRaceBench Benchmark for OpenCL Program Validations

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Recent source codes

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU

GSParLib

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KSig: GPU-accelerated computation of popular time series kernels

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SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

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