high performance computing on graphics processing units: hgpu.org

Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi

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Tags: Benchmarking, Cloud, Computer science, CUDA, Databases, Machine learning, nVidia, Package, Tesla P100

March 6, 2022 by hgpu

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

Shenggan Cheng, Ruidong Wu, Zhongming Yu, Binrui Li, Xiwen Zhang, Jian Peng, Yang You

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Tags: Biology, Computer science, CUDA, Machine learning, nVidia, nVidia A100, Package

March 6, 2022 by hgpu

A ML-based resource utilization OpenCL GPU-kernel fusion model

Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava

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Tags: Computer science, Data parallelism, HPC, Machine learning, nVidia, nVidia GeForce GT 740 M, nVidia GeForce GTX 760, OpenCL

February 20, 2022 by hgpu

gLBM: A GPU enabled Lattice Boltzmann Method Library

Aaron Bray, Rachel B. Clipp, M. Umar Qureshi, Sorin Mitran, Andinet Enquobahrie

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Tags: cfd, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, Package

February 20, 2022 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Migrating CUDA to oneAPI: A Smith-Waterman Case Study

Machine Learning for CUDA+MPI Design Rules

Managing Extreme Heterogeneity in Next Generation HPC Systems

Concurrent CPU-GPU Task Programming using Modern C++

DISTAL: The Distributed Tensor Algebra Compiler

Benchmarking a Proof-of-Concept Performance Portable SYCL-based Fast Fourier Transformation Library

HipBone: A performance-portable GPU-accelerated C++ version of the NekBone benchmark

Integrating SkePU’s algorithmic skeletons with GPI on a cluster

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

A ML-based resource utilization OpenCL GPU-kernel fusion model

gLBM: A GPU enabled Lattice Boltzmann Method Library

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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