high performance computing on graphics processing units: hgpu.org

Jeffrey Krupa, Kelvin Lin, Maria Acosta Flechas, Jack Dinsmore, Javier Duarte, Philip Harris, Scott Hauck, Burt Holzman, Shih-Chieh Hsu, Thomas Klijnsma, Mia Liu, Kevin Pedro, Natchanon Suaysom, Matt Trahms, Nhan Tran

View

Download (PDF)

Source codes

Tags: Deep learning, FPGA, HEP, nVidia, Package, Physics, Tesla T4, Tesla V100

July 26, 2020 by hgpu

UVMBench: A Comprehensive Benchmark Suite for Researching Unified Virtual Memory in GPUs

Yongbin Gu, Wenxuan Wu, Yunfan Li, Lizhong Chen

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 1080 Ti, Package, Performance

July 26, 2020 by hgpu

Offload Annotations: Bringing Heterogeneous Computing to Existing Libraries and Workloads

Gina Yuan, Shoumik Palkar, Deepak Narayanan, Matei Zaharia

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Package, Python, Tesla P100

July 19, 2020 by hgpu

Compyle: a Python package for parallel computing

Aditya Bhosale, Prabhu Ramachandran

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, HPC, nVidia, OpenCL, Package, Python, Tesla P100

July 19, 2020 by hgpu

Deep Graph Library Optimizations for Intel(R) x86 Architecture

Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Package

July 19, 2020 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

* * *

high performance computing on graphics processing units: hgpu.org

Applications

AnyHLS: High-Level Synthesis with Partial Evaluation

Bounds Checking on GPU

OpenSBLI: Automated code-generation for heterogeneous computing architectures applied to compressible fluid dynamics on structured grids

Biomedical and Clinical English Model Packages in the Stanza Python NLP Library

Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM

Darknet on OpenCL: a multi-platform tool for object detection and classification

Bit-level Parallelization of 3DES Encryption on GPU

GPU coprocessors as a service for deep learning inference in high energy physics

UVMBench: A Comprehensive Benchmark Suite for Researching Unified Virtual Memory in GPUs

Offload Annotations: Bringing Heterogeneous Computing to Existing Libraries and Workloads

Compyle: a Python package for parallel computing

Deep Graph Library Optimizations for Intel(R) x86 Architecture

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

Most viewed papers (last 30 days)