high performance computing on graphics processing units: hgpu.org

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Raising the Performance of the Tinker-HP Molecular Modeling Package on Intel’s HPC Architectures: a Living Review [Article v1.0]

Luc-Henri Jolly, Alejandro Duran, Louis Lagardere, Jay W. Ponder, Pengyu Ren, Jean-Philip Piquemal

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Tags: Chemical Physics, Computational Physics, Computer science, Intel Xeon Phi, Mathematical Software, Molecular dynamics, Molecular modeling, OpenMP, Package

June 5, 2019 by hgpu

The Accelerator Wall: Limits of Chip Specialization

Adi Fuchs, David Wentzlaff

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Tags: ASIC, Computer science, FPGA

June 2, 2019 by hgpu

Classify QCD phase transition with deep learning

Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stoecker, Xin-Nian Wang

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Tags: Deep learning, HEP, Machine learning, Neural networks, OpenCL, Package, Pattern recognition, Phase transition, Physics, QCD

June 2, 2019 by hgpu

A Development Platform for Embedded Domain-Specific Languages

Shigeru Chiba, YungYu Zhuang, Thanh-Chung Dao

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Tags: Computer science, CUDA, DSL, High-level Languages, Java, nVidia, Package

June 2, 2019 by hgpu

Heterogeneous Resource-Elastic Scheduling for CPU+FPGA Architectures

Anuj Vaishnav, Khoa Dang Pham, Dirk Koch

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL

June 2, 2019 by hgpu

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Yunfei Teng, Wenbo Gao, Francois Chalus, Anna Choromanska, Donald Goldfarb, Adrian Weller

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080

June 2, 2019 by hgpu

Breadth-First Search using Dynamic Parallelism on the GPU

Dominik Todling

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Tags: Algorithms, Breadth-First Search, Computer science, CUDA, nVidia, Package

May 30, 2019 by hgpu

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Zhenheng Tang, Yuxin Wang, Qiang Wang, Xiaowen Chu

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Tags: Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 2080 Ti, Performance, Tesla P100, Tesla V100

May 30, 2019 by hgpu

Massively Parallel GPU Memory Compaction

Matthias Springer, Hidehiko Masuhara

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan XP, Performance

May 30, 2019 by hgpu

Multi-GPU Rendering with Vulkan API

Lars Olav Tolo

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Tags: AMD Radeon RX 460, ATI, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 970, Package, Thesis, Vulkan

May 26, 2019 by hgpu

Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Simon Garcia De Gonzalo, Sitao Huang, Juan Gomez-Luna, Simon Hammond, Onur Mutlu, Wen-mei Hwu

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Performance, performance portability, Tesla K40, Tesla P100

May 26, 2019 by hgpu

Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel FPGAs

C. Jiang, D. Ojika, T. Kurth, Prabhat, S. Vallecorsa, B. Patel, H. Lam

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

May 26, 2019 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

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

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high performance computing on graphics processing units: hgpu.org

Applications

Raising the Performance of the Tinker-HP Molecular Modeling Package on Intel’s HPC Architectures: a Living Review [Article v1.0]

The Accelerator Wall: Limits of Chip Specialization

Classify QCD phase transition with deep learning

A Development Platform for Embedded Domain-Specific Languages

Heterogeneous Resource-Elastic Scheduling for CPU+FPGA Architectures

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Breadth-First Search using Dynamic Parallelism on the GPU

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Massively Parallel GPU Memory Compaction

Multi-GPU Rendering with Vulkan API

Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel FPGAs

Recent source codes

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

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

DuoReduce: MLIR's benchmark

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