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Decompiling x86 Deep Neural Network Executables

Zhibo Liu, Yuanyuan Yuan, Shuai Wang, Xiaofei Xie, Lei Ma

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

October 9, 2022 by hgpu

Benchmarking optimization algorithms for auto-tuning GPU kernels

Richard Schoonhoven, Ben van Werkhoven, Kees Joost Batenburg

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Tags: AMD Radeon Instinct Mi50, ATI, Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan X, nVidia Titan RTX, OpenCL, Performance, pyCUDA, PyOpenCL, Tesla K20, Tesla P100, Tesla V100

October 9, 2022 by hgpu

An OpenCL-Based FPGA Accelerator for Faster R-CNN

Jianjing An, Dezheng Zhang, Ke Xu, Dong Wang

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Tags: Computational Complexity, Computer science, Deep learning, Design space exploration, FPGA, Neural networks, nVidia, OpenCL, Package, RNN, Tesla K40

October 2, 2022 by hgpu

Enhancing the Performance Portability of Heterogeneous Circuit Analysis Programs

Tsung-Wei Huang

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Tags: AMD Radeon RX 6900 XT, ATI, Computer science, Deep learning, nVidia, nVidia GeForce RTX 3090, OpenCL, performance portability, PyTorch, SYCL

September 11, 2022 by hgpu

Lina: a fast design optimisation tool for software-based FPGA programming

Andre Bannwart Perina

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Tags: Computer science, Design space exploration, FPGA, nVidia, nVidia Quadro K620, OpenCL, Package, Thesis

September 4, 2022 by hgpu

Towards making the most of NLP-based device mapping optimization for OpenCL kernels

Petros Vavaroutsos, Ioannis Oroutzoglou, Dimosthenis Masouros, Dimitrios Soudris

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Tags: AMD Radeon HD 7970, ATI, Computer science, Deep learning, Machine learning, NLP, nVidia, nVidia GeForce GTX 970, OpenCL

September 4, 2022 by hgpu

Towards Understanding and Mitigating Memory-Access Challenges in Computing Systems

Mohammad K. Dashti

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Memory model, nVidia, nVidia GeForce RTX 2070, nVidia Jetson TK1, OpenCL, Package, Thesis

August 28, 2022 by hgpu

Exploring Thread Coarsening on FPGA

Mostafa Eghbali Zarch, Reece Neff, Michela Becchi

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Tags: Compilers, Computer science, FPGA, OpenCL, Performance, performance portability

August 28, 2022 by hgpu

BenchPress: A Deep Active Benchmark Generator

Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu, Chris Cummins, Kim Hazelwood, Ajitha Rajan, Hugh Leather

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Tags: Benchmarking, Code generation, Computer science, Deep learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, PyTorch

August 21, 2022 by hgpu

Performance analysis of matrix-free conjugate gradient kernels using SYCL

Igor Baratta, Chris Richardson, Garth Wells

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Tags: Benchmarking, Computer science, Latency, nVidia, nVidia A100, OpenCL, Package, Performance, SYCL

August 21, 2022 by hgpu

Optimization of GPU workloads using natural language processing based on deep learning techniques

Petros Vavaroutsos

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 970, OpenCL, Thesis

August 21, 2022 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

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

Decompiling x86 Deep Neural Network Executables

Benchmarking optimization algorithms for auto-tuning GPU kernels

An OpenCL-Based FPGA Accelerator for Faster R-CNN

Enhancing the Performance Portability of Heterogeneous Circuit Analysis Programs

Lina: a fast design optimisation tool for software-based FPGA programming

Towards making the most of NLP-based device mapping optimization for OpenCL kernels

Towards Understanding and Mitigating Memory-Access Challenges in Computing Systems

Exploring Thread Coarsening on FPGA

BenchPress: A Deep Active Benchmark Generator

Performance analysis of matrix-free conjugate gradient kernels using SYCL

Optimization of GPU workloads using natural language processing based on deep learning techniques

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

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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