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Design Space Exploration of Concurrency Mapping to FPGAs in Weather and Climate Applications with Xilinx SDSoC OpenCL, SDSoC C++ and Vivad

Moteb Salem Alghamdi

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Tags: Computer science, Design space exploration, FPGA, HLS, HPC, OpenCL, Thesis

November 27, 2022 by hgpu

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Manuel Costanzo, Enzo Rucci, Carlos García Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, Heterogeneous systems, Intel, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3090, OpenCL, Sequence alignment, SYCL, Tesla V100

November 27, 2022 by hgpu

Training a Vision Transformer from scratch in less than 24 hours with 1 GPU

Saghar Irandoust, Thibaut Durand, Yunduz Rakhmangulova, Wenjie Zi, Hossein Hajimirsadeghi

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Tags: Benchmarking, Computer science, Computer vision, Deep learning

November 20, 2022 by hgpu

Hardware Checkpointing and Productive Debugging Flows for FPGAs

Sameh Attia

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Tags: Computer science, FPGA, Package, Thesis

November 20, 2022 by hgpu

Challenges and Techniques for Transparent Acceleration of Unmodified Big Data Applications

Maria Nektaria Xekalaki

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Tags: Benchmarking, Computer science, CUDA, FPGA, Heterogeneous systems, Java, nVidia, nVidia GeForce GTX 1060, nVidia Quadro GP100, OpenCL, Package, Performance, Tesla V100, Thesis

November 20, 2022 by hgpu

TorchOpt: An Efficient Library for Differentiable Optimization

Jie Ren, Xidong Feng, Bo Liu, Xuehai Pan, Yao Fu, Luo Mai, Yaodong Yang

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Tags: Computer science, CUDA, Deep learning, Mathematical Software, nVidia, OpenMP, Package, PyTorch

November 20, 2022 by hgpu

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Richard Schoonhoven, Bram Veenboer, Ben van Werkhoven, Kees Joost Batenburg

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia RTX A4000, OpenCL, Package, Tesla A100

November 20, 2022 by hgpu

Capturing the Memory Topology of GPUs

Dominik Größler

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce 840 M, nVidia GeForce RTX 2080 Ti, nVidia Quadro P 6000, Package, Performance, PTX, Tesla K20, Tesla V100, Thesis

November 13, 2022 by hgpu

A Study on Neural-based Code Summarization in Low-resource Settings

Yang He

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Tags: Computer science, CUDA, Deep learning, nVidia, PyTorch, Software Engineering, Tesla V100, Thesis

November 13, 2022 by hgpu

pyGSL: A Graph Structure Learning Toolkit

Max Wasserman, Gonzalo Mateos

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Tags: Computer science, Machine learning, Package, PyTorch, Signal processing

November 13, 2022 by hgpu

iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

Fei Xu, Jianian Xu, Jiabin Chen, Li Chen, Ruitao Shang, Zhi Zhou, Fangming Liu

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Tags: Cloud, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla V100

November 13, 2022 by hgpu

Enabling Data Movement and Computation Pipelining in Deep Learning Compiler

Guyue Huang, Yang Bai, Liu Liu, Yuke Wang, Bei Yu, Yufei Ding, Yuan Xie

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia A100

November 6, 2022 by hgpu

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

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high performance computing on graphics processing units: hgpu.org

Design Space Exploration of Concurrency Mapping to FPGAs in Weather and Climate Applications with Xilinx SDSoC OpenCL, SDSoC C++ and Vivad

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Training a Vision Transformer from scratch in less than 24 hours with 1 GPU

Hardware Checkpointing and Productive Debugging Flows for FPGAs

Challenges and Techniques for Transparent Acceleration of Unmodified Big Data Applications

TorchOpt: An Efficient Library for Differentiable Optimization

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Capturing the Memory Topology of GPUs

A Study on Neural-based Code Summarization in Low-resource Settings

pyGSL: A Graph Structure Learning Toolkit

Enabling Data Movement and Computation Pipelining in Deep Learning Compiler

Recent source codes

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

AutoDock for GPUs and other accelerators

spotPython: Sequential Parameter Optimization in Python

Test benchmarks for CELERITY

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Redwood heterogeneous framework

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