high performance computing on graphics processing units: hgpu.org

Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiang Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, Hao Su

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Titan RTX, Package, Rendering, Robotics

February 12, 2023 by hgpu

Evaluation of Rust for GPGPU high-performance computing

Viktor Franzén, Carl Östling

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Tags: Computer science, CUDA, HPC, nVidia, nVidia GeForce GTX 1660 Ti, nVidia GeForce RTX 2080 Ti, Package, PTX, Thesis

February 5, 2023 by hgpu

CMLCompiler: A Unified Compiler for Classical Machine Learning

Xu Wen, Wanling Gao, Anzheng Li, Lei Wang, Zihan Jiang, Jianfeng Zhan

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Tags: Compilers, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia Titan RTX

February 5, 2023 by hgpu

Revisiting Query Performance in GPU Database Systems

Jiashen Cao, Rathijit Sen, Matteo Interlandi, Joy Arulraj, Hyesoon Kim

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Tags: Computer science, CUDA, Databases, nVidia, nVidia A100

February 5, 2023 by hgpu

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

Kazuaki Matsumura, Simon Garcia De Gonzalo, Antonio J. Peña

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Tags: Benchmarking, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, OpenACC, Package, PTX, Tesla K40, Tesla K80, Tesla V100

February 5, 2023 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

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high performance computing on graphics processing units: hgpu.org

EvoTorch: Scalable Evolutionary Computation in Python

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

Porting numerical integration codes from CUDA to oneAPI: a case study

Auto-SpMV: Automated Optimizing SpMV Kernels on GPU

Orca: FSS-based Secure Training with GPUs

GPU Offloading in ExaHyPE Through C++ Standard Algorithms

TLP: A Deep Learning-based Cost Model for Tensor Program Tuning

ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills

Evaluation of Rust for GPGPU high-performance computing

CMLCompiler: A Unified Compiler for Classical Machine Learning

Revisiting Query Performance in GPU Database Systems

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

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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