high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia A40

Gaining Cross-Platform Parallelism for HAL’s Molecular Dynamics Package using SYCL

Viktor Skoblin, Felix Höfling, Steffen Christgau

View

Tags: AMD Radeon Instinct MI210, ATI, Computer science, CUDA, Molecular dynamics, nVidia, nVidia A100, nVidia A40, Package, Physics, SYCL

June 9, 2024 by hgpu

Gallatin: A General-Purpose GPU Memory Manager

Hunter McCoy, Prashant Pandey

View

Tags: Computer science, CUDA, HPC, Memory, nVidia, nVidia A40, Package

February 4, 2024 by hgpu

Domain-Specific Code Language Models: Unraveling the Potential for HPC Codes and Tasks

Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Mihai Capota, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren

View

Tags: AI, Code generation, Computer science, Heterogeneous systems, HPC, nVidia, nVidia A40, OpenMP, Package, Python

January 7, 2024 by hgpu

cuSZ-I: High-Fidelity Error-Bounded Lossy Compression for Scientific Data on GPUs

Jinyang Liu, Jiannan Tian, Shixun Wu, Sheng Di, Boyuan Zhang, Yafan Huang, Kai Zhao, Guanpeng Li, Dingwen Tao, Zizhong Chen, Franck Cappello

View

Tags: Compression, Computer science, CUDA, nVidia, nVidia A100, nVidia A40

December 18, 2023 by hgpu

Scope is all you need: Transforming LLMs for HPC Code

Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren

View

Tags: Code generation, Computer science, Deep learning, HPC, nVidia, nVidia A40, nVidia H100, OpenMP, Package

September 6, 2023 by hgpu

Leveraging Memory Copy Overlap for Efficient Sparse Matrix-Vector Multiplication on GPUs

Guangsen Zeng, Yi Zou

View

Tags: Computer science, CUDA, Machine learning, nVidia, nVidia A40, Sparse matrix

September 6, 2023 by hgpu

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

Hyun Park, Parth Patel, Roland Haas, E. A. Huerta

View

Tags: AI, Algorithms, Biology, Biomolecules, Biophysics, nVidia, nVidia A100, nVidia A40, Package

August 20, 2023 by hgpu

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer

View

Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, nVidia, nVidia A40, Package, PyTorch

August 21, 2022 by hgpu

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

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

LLaMA-Inference-Bench

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

MATAR

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

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

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

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

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

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

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

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: