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hgpu.org » Heterogeneous systems

Optimising Convolutional Neural Networks Inference on Low-Powered GPUs

Simon Rovder, Jose Cano, Michael O'Boyle

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Tags: Computer science, Deep learning, Heterogeneous systems, Neural networks, OpenCL

February 10, 2019 by hgpu

Heterogeneous Distributed Big Data Clustering on Sparse Grids

David Pfander, Gregor Daiss, Dirk Pfluger

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Tags: Clustering, Computer science, Data mining, Distributed computing, Heterogeneous systems, Machine learning, MPI, nVidia, OpenCL, Package, performance portability, Tesla P100

February 10, 2019 by hgpu

Exploiting OpenMP & OpenACC to Accelerate a Molecular Docking Mini-App in Heterogeneous HPC Nodes

Emanuele Vitali, Davide Gadioli, Gianluca Palermo, Andrea Beccari, Carlo Cavazzoni, Cristina Silvano

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, molecular docking, MPI, nVidia, OpenACC, OpenMP, Tesla K80

January 27, 2019 by hgpu

Direct N-body code on low-power embedded ARM GPUs

David Goz, Sara Bertocco, Luca Tornatore, Giuliano Taffoni

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Tags: ARM, Astrophysics, FPGA, Heterogeneous systems, Instrumentation and Methods for Astrophysics, N-body simulation, OpenCL, OpenMPI, SoC

January 27, 2019 by hgpu

Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems

B. P'erez, E. Stafford, J. L. Bosque, R. Beivide, S. Mateo, X. Teruel, X. Martorell, E. Ayguad'e

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Tags: Computer science, Heterogeneous systems, nVidia, OmpSs, OpenCL, Tesla K20

January 13, 2019 by hgpu

HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing

Yi-Hsiang Lai, Yuze Chi, Yuwei Hu, Jie Wang, Cody Hao Yu, Yuan Zhou, Jason Cong, Zhiru Zhang

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Tags: Algorithms, Compilers, Computer science, FPGA, Heterogeneous systems, HLS, OpenCL, Python

January 6, 2019 by hgpu

HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines

Periklis Chrysogelos, Manos Karpathiotakis, Raja Appuswamy, Anastasia Ailamaki

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Tags: Compilers, Computer science, CUDA, Databases, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080

January 6, 2019 by hgpu

Towards Automatic Transformation of Legacy Scientific Code into OpenCL for Optimal Performance on FPGAs

Wim Vanderbauwhede, Syed Waqar Nabi

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Tags: Compilers, Computer science, Fortran, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenACC, OpenCL, OpenMP, Package

January 6, 2019 by hgpu

Automatic Performance Optimization on Heterogeneous Computer Systems using Manycore Coprocessors

Chenggang Lai

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, MPI, OpenMP, Thesis

December 30, 2018 by hgpu

On Runtime Systems for Task-based Programming on Heterogeneous Platforms

Samuel Thibault

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia Quadro FX 5800, OpenCL, Operating systems, StarPU, Task scheduling, Tesla C2050, Tesla K20, Tesla M2075, Thesis

December 23, 2018 by hgpu

Clacc: Translating OpenACC to OpenMP in Clang

Joel E. Denny, Seyong Lee, Jeffrey S. Vetter

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Tags: Compilers, Computer science, Heterogeneous systems, LLVM, nVidia, nVidia GeForce GTX 1050, OpenACC, OpenMP

December 9, 2018 by hgpu

ASW: Accelerating Smith-Waterman Algorithm on Coupled CPU-GPU Architecture

Huihui Zou, Shanjiang Tang, Ce Yu, Hao Fu, Yusen Li, Wenjie Tang

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Tags: Algorithms, APU, Bioinformatics, Biology, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 750, OpenCL, Sequence alignment, Smith-Waterman algorithm

December 9, 2018 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Optimising Convolutional Neural Networks Inference on Low-Powered GPUs

Heterogeneous Distributed Big Data Clustering on Sparse Grids

Exploiting OpenMP & OpenACC to Accelerate a Molecular Docking Mini-App in Heterogeneous HPC Nodes

Direct N-body code on low-power embedded ARM GPUs

Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems

HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing

HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines

Towards Automatic Transformation of Legacy Scientific Code into OpenCL for Optimal Performance on FPGAs

Automatic Performance Optimization on Heterogeneous Computer Systems using Manycore Coprocessors

On Runtime Systems for Task-based Programming on Heterogeneous Platforms

Clacc: Translating OpenACC to OpenMP in Clang

ASW: Accelerating Smith-Waterman Algorithm on Coupled CPU-GPU Architecture

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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