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Dataloader Parameter Tuner: An Automated Dataloader Parameter Tuner for Deep Learning Models

JooYoung Park, DoangJoo Synn, XinYu Piao, Jong-Kook Kim

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 3080 Ti, Performance, Prefetch

October 16, 2022 by hgpu

Tensor Computation Based on Heterogeneous Memory

Jiawen Liu

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Tags: Computer science, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Prefetch, Thesis

July 3, 2022 by hgpu

A readahead prefetcher for GPU file system layer

Vasilis Dimitsas, Mark Silberstein

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Tags: Benchmarking, Computer science, CUDA, nVidia, Prefetch, Tesla K40

September 19, 2021 by hgpu

Performance Evaluation of Advanced Features in CUDA Unified Memory

Steven W. D. Chien, Ivy B. Peng, Stefano Markidis

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 1050, Package, Prefetch, Tesla V100

October 27, 2019 by hgpu

Energy Efficient Computing on Multi-core Processors: Vectorization and Compression Techniques

Abdullah Al Hasib

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Tags: Compression, Computer science, Data parallelism, Intel Xeon Phi, Prefetch, Programming techniques, Thesis

June 20, 2018 by hgpu

Software Prefetching for Indirect Memory Accesses

Sam Ainsworth, Timothy M. Jones

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Tags: ARM, Benchmarking, Compilers, Computer science, Intel Xeon Phi, LLVM, MPI, Package, Prefetch

January 10, 2017 by hgpu

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Tarun Beri

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Tags: Computer science, CUDA, FFT, GPU cluster, Linear Algebra, Matrix multiplication, nVidia, Package, Prefetch, Tesla M2070, Thesis

November 5, 2016 by hgpu

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

Andreas Klockner, Lucas C. Wilcox, T. Warburton

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Code generation, Computer science, Fortran, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Performance, Prefetch, Programming Languages, Python, Tesla K40

April 29, 2016 by hgpu

Breadth First Search Vectorization on the Intel Xeon Phi

Mireya Paredes, Graham Riley, Mikel Lujan

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Tags: Algorithms, Computer science, Databases, Intel Xeon Phi, Prefetch

April 14, 2016 by hgpu

Unlocking Bandwidth for GPUs in CC-NUMA Systems

Neha Agarwal, David Nellans, Mike O'Connor, Stephen W. Keckler, Thomas F. Wenisch

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia GeForce GTX 480, Prefetch

February 6, 2015 by hgpu

Supporting Applications Involving Dynamic Data Structures and Irregular Memory Access on Emerging Parallel Platforms

Bin Ren

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, Memory model, nVidia, nVidia Quadro FX 5800, Prefetch, Tesla C2050, Thesis

September 19, 2014 by hgpu

Dynamic Data Management Among Multiple Databases for Optimization of Parallel Computations in Heterogeneous HPC Systems

Pawel Rosciszewski

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Tags: Computer science, Databases, Heterogeneous systems, nVidia, nVidia Quadro FX 3800, OpenCL, Prefetch, Tesla C2050

July 30, 2014 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

Dataloader Parameter Tuner: An Automated Dataloader Parameter Tuner for Deep Learning Models

Tensor Computation Based on Heterogeneous Memory

A readahead prefetcher for GPU file system layer

Performance Evaluation of Advanced Features in CUDA Unified Memory

Energy Efficient Computing on Multi-core Processors: Vectorization and Compression Techniques

Software Prefetching for Indirect Memory Accesses

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

Breadth First Search Vectorization on the Intel Xeon Phi

Unlocking Bandwidth for GPUs in CC-NUMA Systems

Supporting Applications Involving Dynamic Data Structures and Irregular Memory Access on Emerging Parallel Platforms

Dynamic Data Management Among Multiple Databases for Optimization of Parallel Computations in Heterogeneous HPC Systems

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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