high performance computing on graphics processing units: hgpu.org

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

Over-synchronization in GPU Programs

Ajay Nayak, Arkaprava Basu

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, Package, Performance, PTX

November 10, 2024 by hgpu

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

Pascal Jahan Elahi

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Tags: Computer science, CUDA, MPI, nVidia, nVidia GH200, OpenMP, Package, Performance

November 10, 2024 by hgpu

NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference

Xuanlin Jiang, Yang Zhou, Shiyi Cao, Ion Stoica, Minlan Yu

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Tags: Artificial intelligence, Computer science, CUDA, LLM, Memory, nVidia, nVidia A10, nVidia H100, Tesla T4

November 10, 2024 by hgpu

LLload: An Easy-to-Use HPC Utilization Tool

Chansup Byun, Albert Reuther, Julie Mullen, LaToya Anderson, William Arcand, Bill Bergeron, David Bestor, Alexander Bonn, Daniel Burrill, Vijay Gadepally, Michael Houle, Matthew Hubbell, Hayden Jananthan, Michael Jones, Piotr Luszczek, Peter Michaleas, Lauren Milechin, Guillermo Morales, Andrew Prout, Antonio Rosa, Charles Yee, Jeremy Kepner

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Tags: Computer science, CUDA, HPC, nVidia, Performance

November 3, 2024 by hgpu

Scheduling Languages: A Past, Present, and Future Taxonomy

Mary Hall, Cosmin Oancea, Anne C. Elster, Ari Rasch, Sameeran Joshi, Amir Mohammad Tavakkoli, Richard Schulze

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Tags: Auto-Tuning, Compilers, Computer science, Performance, Programming Languages, Task scheduling

November 3, 2024 by hgpu

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high performance computing on graphics processing units: hgpu.org

Applications

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

Over-synchronization in GPU Programs

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference

LLload: An Easy-to-Use HPC Utilization Tool

Scheduling Languages: A Past, Present, and Future Taxonomy

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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