high performance computing on graphics processing units: hgpu.org

Jianhui Li, Zhennan Qin, Yijie Mei, Jingze Cui, Yunfei Song, Ciyong Chen, Yifei Zhang, Longsheng Du, Xianhang Cheng, Baihui Jin, Jason Ye, Eric Lin, Dan Lavery

View

Download (PDF)

Source codes

Tags: Code generation, Compilers, Computer science, Deep learning, OpenCL, Package

January 8, 2023 by hgpu

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

Erik Hellsten, Artur Souza, Johannes Lenfers, Rubens Lacouture, Olivia Hsu, Adel Ejjeh, Fredrik Kjolstad, Michel Steuwer, Kunle Olukotun, Luigi Nardi

View

Download (PDF)

Source codes

Tags: Bayesian, Compilers, Computer science, FPGA, nVidia, OpenCL, Package, Performance, Programming Languages, Tesla K80

January 8, 2023 by hgpu

Arax: a runtime framework for decoupling applications from heterogeneous accelerators

Manos Pavlidakis, Stelios Mavridis, Antony Chazapis, Giorgos Vasiliadis, Angelos Bilas

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce RTX 2080, OpenCL, Package

January 8, 2023 by hgpu

A Framework to Generate High-Performance Time-stepped Agent-based Simulations on Heterogeneous Hardware

Jiajian Xiao

View

Download (PDF)

Tags: Computer science, FPGA, Heterogeneous systems, nVidia GeForce GTX 1060, nVidia GeForce RTX 2080 Ti, OpenCL, Thesis

December 19, 2022 by hgpu

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs

Improving Performance of Hardware Accelerators by Optimizing Data Movement: A Bioinformatics Case Study

Optimization of massive data applications on heterogeneous architectures

Pulsar search acceleration using FPGAs and OpenCL templates

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Efficient OpenCL system integration of non-blocking FPGA accelerators

Improving the scalability of modern applications by parallel multi-core and many-core programming

A Domain-Extensible Compiler with Controllable Automation of Optimisations

oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

Arax: a runtime framework for decoupling applications from heterogeneous accelerators

A Framework to Generate High-Performance Time-stepped Agent-based Simulations on Heterogeneous Hardware

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Most viewed papers (last 30 days)