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hgpu.org » Heterogeneous systems

SYnergy: Fine-grained Energy-Efficient Heterogeneous Computing for Scalable Energy Saving

Kaijie Fan, Marco D'Antonio, Lorenzo Carpentieri, Biagio Cosenza, Federico Ficarelli, Daniele Cesarini

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, Energy-efficient computing, GPU cluster, Heterogeneous systems, Machine learning, nVidia, nVidia V100, SYCL

August 13, 2023 by hgpu

Efficiency without Tears: Securing Multilingual Programs with TRINITY

Hao Zhu, Baojian Hua, Xinrong Lin, Yufei Wu

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Tags: Computer science, Databases, Heterogeneous systems, HPC, Machine learning, OpenCL, Security

July 30, 2023 by hgpu

Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

Polykarpos Thomadakis

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, Nuclear physics, nVidia, OpenCL, Physics, Task scheduling, Tesla V100, Thesis

July 16, 2023 by hgpu

Improving the Performance, Portability, and Productivity of Hardware Accelerators

Pablo Antonio Martínez Sánchez

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Tags: Computer science, CUDA, Heterogeneous systems, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 2080 Ti, Performance, performance portability, Thesis

July 16, 2023 by hgpu

Out-of-the-box library support for DBMS operations on GPUs

Harish Kumar Harihara Subramanian, Bala Gurumurthy, Gabriel Campero Durand, David Broneske, Gunter Saake

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Tags: Computer science, Databases, Hashing, Heterogeneous systems, nVidia, nVidia GeForce RTX 2080 Ti, nVidia V100, OpenCL, Package, Performance

July 2, 2023 by hgpu

Managing, Profiling, and Optimizing Heterogeneous GPU Workloads

James Gleeson

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Tags: Computer science, CUDA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 480, nVidia GeForce RTX 2080 Ti, nVidia Grid K1, Optimization, Package, Performance, Tesla V100, Thesis, Virtualization

July 2, 2023 by hgpu

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Aurélien François Gilbert Bloch

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Tags: Compilers, Computer science, CUDA, Design space exploration, DSP, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1660, nVidia GeForce RTX 3080 Ti, Package, Thesis

June 25, 2023 by hgpu

GPU First – Execution of Legacy CPU Codes on GPUs

Shilei Tian, Tom Scogland, Barbara Chapman, Johannes Doerfert

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Tags: Benchmarking, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia A100, OpenMP

June 25, 2023 by hgpu

Implementation Techniques for SPMD Kernels on CPUs

Joachim Meyer, Aksel Alpay, Sebastian Hack, Holger Fröning, Vincent Heuveline

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, performance portability

June 4, 2023 by hgpu

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

Improving Energy Efficiency of Basic Linear Algebra Routines on Heterogeneous Systems with Multiple GPUs

Hadi Zamani Sabzi

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Factorization, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 1660, nVidia GeForce GTX 980, Thesis

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

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

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high performance computing on graphics processing units: hgpu.org

SYnergy: Fine-grained Energy-Efficient Heterogeneous Computing for Scalable Energy Saving

Efficiency without Tears: Securing Multilingual Programs with TRINITY

Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

Improving the Performance, Portability, and Productivity of Hardware Accelerators

Out-of-the-box library support for DBMS operations on GPUs

Managing, Profiling, and Optimizing Heterogeneous GPU Workloads

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

GPU First – Execution of Legacy CPU Codes on GPUs

Implementation Techniques for SPMD Kernels on CPUs

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

Improving Energy Efficiency of Basic Linear Algebra Routines on Heterogeneous Systems with Multiple GPUs

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

Recent source codes

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

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