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Programming Heterogeneous Systems with General and Domain-Specific Frameworks

Lukas Sommer

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 2070, OpenCL, OpenMP, Package, Python, Thesis

November 21, 2021 by hgpu

QGTC: Accelerating Quantized GNN via GPU Tensor Core

Yuke Wang, Boyuan Feng, Yufei Ding

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce RTX 3090, TPU

November 21, 2021 by hgpu

Accelerating JPEG Decompression on GPUs

André Weißenberger, Bertil Schmidt

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Tags: Compression, CUDA, Image processing, nVidia, nVidia A100

November 21, 2021 by hgpu

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

Zane Fink, Simeng Liu, Jaemin Choi, Matthias Diener, Laxmikant V. Kale

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Tags: Benchmarking, Computer science, CUDA, Distributed computing, HPC, Machine learning, MPI, nVidia, Package, Performance, Python, Tesla V100

November 14, 2021 by hgpu

Collage: Automated Integration of Deep Learning Backends

Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 2070, Tesla V100

November 7, 2021 by hgpu

iGUARD: In-GPU Advanced Race Detection

Aditya K Kamath, Arkaprava Basu

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Tags: Computer science, CUDA, nVidia, nVidia Titan RTX, Package, Performance

November 7, 2021 by hgpu

Principles towards Real-Time Simulation of Material Point Method on Modern GPUs

Yun Fei, Yuhan Huang, Ming Gao

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Tags: Computer science, CUDA, Graphics, nVidia, nVidia DGX-1, Physics-based simulation, Tesla V100

November 7, 2021 by hgpu

Improving Performance and Energy Efficiency of GPUs through Locality Analysis

Devashree Tripathy

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, Performance, PTX, Thesis

October 31, 2021 by hgpu

Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

Zhengda Bian, Hongxin Liu, Boxiang Wang, Haichen Huang, Yongbin Li, Chuanrui Wang, Fan Cui, Yang You

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Tags: Computer science, CUDA, Data parallelism, Deep learning, nVidia, Package, Tesla P100

October 31, 2021 by hgpu

Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance

Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, Yibo Zhu

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Neural networks, nVidia, Performance, Tesla T4

October 31, 2021 by hgpu

The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding

Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

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Tags: Compilers, Computer science, CUDA, Deep learning, nVidia, Tesla V100

October 24, 2021 by hgpu

Monitoring Collective Communication Among GPUs

Muhammet Abdullah Soyturk, Palwisha Akhtar, Erhan Tezcan, Didem Unat

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Tags: Computer science, CUDA, MPI, nVidia, Package, Performance, Tesla V100

October 24, 2021 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming Heterogeneous Systems with General and Domain-Specific Frameworks

QGTC: Accelerating Quantized GNN via GPU Tensor Core

Accelerating JPEG Decompression on GPUs

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

Collage: Automated Integration of Deep Learning Backends

iGUARD: In-GPU Advanced Race Detection

Principles towards Real-Time Simulation of Material Point Method on Modern GPUs

Improving Performance and Energy Efficiency of GPUs through Locality Analysis

Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance

The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding

Monitoring Collective Communication Among GPUs

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

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