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

Zhiyi Zhang, Pengfei Zhang, Qi Wang

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Java, nVidia, nVidia GeForce RTX 3060 Ti, Package

May 21, 2023 by hgpu

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

Juan Fumero, György Rethy, Athanasios Stratikopoulos, Nikos Foutris, Christos Kotselidis

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Tags: Code generation, Computer science, Heterogeneous systems, Java, OpenCL, Package

May 21, 2023 by hgpu

Prediction of Performance and Power Consumption of GPGPU Applications

Gargi Alavani, Santonu Sarkar

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Tags: Computer science, CUDA, Energy-efficient computing, Java, Machine learning, nVidia, Package, Performance, PTX, Tesla K20, Thesis

May 14, 2023 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

GPU-based JSON data processing using structural indexes

Koen R. Vlaswinkel

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Tags: Computer science, CUDA, Java, nVidia, nVidia GeForce GTX 1080, Package, Python, Thesis

May 15, 2022 by hgpu

Can We Run in Parallel? Automating Loop Parallelization for TornadoVM

Rishi Sharma, Shreyansh Kulshreshtha, Manas Thakur

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Tags: Computer science, FPGA, Heterogeneous systems, Java, OpenCL

May 15, 2022 by hgpu

Data transfer optimizations for heterogeneous managed runtime systems

Florin-Gabriel Blanaru

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Tags: Computer science, CUDA, Heterogeneous systems, Java, nVidia, nVidia GeForce GTX 1650, nVidia Quadro GP100, OpenCL

March 27, 2022 by hgpu

Fancier: A Unified Framework for Java, C, and OpenCL Integration

Sergio Afonso, Francisco Almeida

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Tags: Computer science, Image processing, Java, OpenACC, OpenCL, Package, Performance

January 16, 2022 by hgpu

Performance Optimisations for Heterogeneous Managed Runtime Systems

Michail Papadimitriou

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Tags: Computer science, FPGA, Heterogeneous systems, Java, nVidia, nVidia GeForce GTX 1650, OpenCL, Performance, Thesis

November 14, 2021 by hgpu

Automatically Exploiting the Memory Hierarchy of GPUs through Just-in-Time Compilation

Michail Papadimitriou, Juan Fumero, Athanasios Stratikopoulos, Christos Kotselidis

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Tags: Benchmarking, Computer science, Java, nVidia, nVidia GeForce GTX 1650, OpenCL, Package

May 23, 2021 by hgpu

Multiple-Tasks on Multiple-Devices (MTMD): Exploiting Concurrency in Heterogeneous Managed Runtimes

Michail Papadimitriou, Eleni Markou, Juan Fumero, Athanasios Stratikopoulos, Florin Blanaru, Christos Kotselidis

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Tags: Computer science, Heterogeneous systems, Java, Machine learning, nVidia, nVidia GeForce GTX 1650, OpenCL

April 11, 2021 by hgpu

Transparent Compiler and Runtime Specializations for Accelerating Managed Languages on FPGAs

Michail Papadimitriou, Juan Fumero, Athanasios Stratikopoulos, Foivos S. Zakkak, Christos Kotselidis

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Tags: Computer science, FPGA, HLS, Java, OpenCL, Optimization, Package, Programming Languages

November 8, 2020 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

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

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

Prediction of Performance and Power Consumption of GPGPU Applications

Challenges and Techniques for Transparent Acceleration of Unmodified Big Data Applications

GPU-based JSON data processing using structural indexes

Can We Run in Parallel? Automating Loop Parallelization for TornadoVM

Data transfer optimizations for heterogeneous managed runtime systems

Fancier: A Unified Framework for Java, C, and OpenCL Integration

Performance Optimisations for Heterogeneous Managed Runtime Systems

Automatically Exploiting the Memory Hierarchy of GPUs through Just-in-Time Compilation

Multiple-Tasks on Multiple-Devices (MTMD): Exploiting Concurrency in Heterogeneous Managed Runtimes

Transparent Compiler and Runtime Specializations for Accelerating Managed Languages on FPGAs

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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