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hgpu.org » Optimization

A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)

Chellammal Surianarayanan, John Jeyasekaran Lawrence, Pethuru Raj Chelliah, Edmond Prakash, Chaminda Hewage

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Tags: Artificial intelligence, Computer science, Energy-efficient computing, Optimization, survey

February 12, 2023 by hgpu

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

Yi Zhai, Yu Zhang, Shuo Liu, Xiaomeng Chu, Jie Peng, Jianmin Ji, Yanyong Zhang

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Tags: Computer science, CUDA, Deep learning, nVidia, Optimization, Package, Tesla K80, Tesla T4

February 12, 2023 by hgpu

Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

Francesco Barchi, Emanuele Parisi, Andrea Bartolini, Andrea Acquaviva

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Tags: AMD Radeon HD 7970, ATI, Computer science, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Optimization, Overview, Security

August 7, 2022 by hgpu

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

Weile Wei

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Package, QMC, Tesla V100, Thesis

July 3, 2022 by hgpu

MapReduce for Counting Word Frequencies with MPI and GPUs

Nithin Kavi

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Tags: Algorithms, Computer science, CUDA, Julia, MapReduce, MPI, NLP, nVidia, Optimization, Package, Tesla V100

June 19, 2022 by hgpu

ALPINIST: An Annotation-Aware GPU Program Optimizer

Ömer Sakar, Mohsen Safari, Marieke Huisman, Anton Wijs

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Tags: Computer science, CUDA, nVidia, OpenCL, Optimization, Package

April 10, 2022 by hgpu

Intel oneAPI DPC++ FPGA Optimization Guide

Intel Corporation

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Tags: Computer science, FPGA, Optimization, SYCL, Whitepaper

October 3, 2021 by hgpu

Using Workload Characterization to Guide High Performance Graph Processing

Mohamed W. Hassan

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Tags: Computer science, FPGA, Heterogeneous systems, HLS, nVidia, OpenCL, Optimization, Tesla K20, Thesis

May 30, 2021 by hgpu

Performance Assessment of using OpenCL on FPGA Systems for ODE Solvers

Patrick Tobias Haft

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Tags: Code generation, Computational biology, Computer science, Differential equations, FPGA, nVidia, nVidia GeForce GTX 980 Ti, ODEs, OpenCL, Optimization, Ordinary differential equations, Tesla K20, Thesis

May 16, 2021 by hgpu

Accelerating Deep Neural Networks implementation: A survey

Meriam Dhouibi, Ahmed Karim Ben Salem, Afef Saidi, Slim Ben Saoud

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Tags: ASIC, Computer science, Deep learning, FPGA, HLS, Neural networks, OpenCL, Optimization, Performance, survey

March 28, 2021 by hgpu

A Deep Learning Based Cost Model for Automatic Code Optimization

Riyadh Baghdadi, Massinissa Merouani, Mohamed-Hicham Leghettas, Kamel Abdous, Taha Arbaoui, Karima Benatchba, Saman Amarasinghe

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Tags: Compilers, Computer science, Deep learning, Optimization, Performance

March 21, 2021 by hgpu

ECM modeling and performance tuning of SpMV and Lattice QCD on A64FX

Christie Alappat, Nils Meyer, Jan Laukemann, Thomas Gruber, Georg Hager, Gerhard Wellein, Tilo Wettig

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Tags: Computer science, CUDA, nVidia, Optimization, Package, Physics, QCD, Sparse matrix, Tesla V100

March 14, 2021 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

A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)

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

Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

MapReduce for Counting Word Frequencies with MPI and GPUs

ALPINIST: An Annotation-Aware GPU Program Optimizer

Intel oneAPI DPC++ FPGA Optimization Guide

Using Workload Characterization to Guide High Performance Graph Processing

Performance Assessment of using OpenCL on FPGA Systems for ODE Solvers

Accelerating Deep Neural Networks implementation: A survey

A Deep Learning Based Cost Model for Automatic Code Optimization

ECM modeling and performance tuning of SpMV and Lattice QCD on A64FX

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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