high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications

OpenACC cache Directive: Opportunities and Optimizations

Ahmad Lashgar, Amirali Baniasadi

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Tags: Code generation, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Performance, Tesla K20

December 3, 2016 by hgpu

A Real-time Single Pulse Detection Algorithm for GPUs

Karel Adamek, Wesley Armour

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Tags: Algorithms, Astrophysics, CUDA, Filtering, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX Titan X, Package, Signal processing

December 3, 2016 by hgpu

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

Amir Alansary, Bernhard Kainz, Martin Rajchl, Maria Murgasova, Mellisa Damodaram, David F.A. Lloyd, Alice Davidson, Steven G. McDonagh, Mary Rutherford, Joseph V. Hajnal, Daniel Rueckert

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Tags: Computer science, CUDA, Magnetic resonance imaging, Motion compensation, MRI, nVidia, Package, Tesla K40

November 25, 2016 by hgpu

Fast and Energy-Efficient CNN Inference on IoT Devices

Mohammad Motamedi, Daniel Fong, Soheil Ghiasi

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Tags: CNN, Computer science, Deep learning, Java, Machine learning, Neural networks, Package, RenderScript, SoC

November 25, 2016 by hgpu

A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications

Matthew W. Moskewicz, Ali Jannesari, Kurt Keutzer

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Tags: AMD R9 Nano, ARM, ATI, Code generation, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

November 23, 2016 by hgpu

GPU-accelerated Red Blood Cells Simulations with Transport Dissipative Particle Dynamics

Ansel L. Blumers, Yu-Hang Tang, Zhen Li, Xuejin Li, George E. Karniadakis

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Tags: Biological Physics, Computational Physics, CUDA, MPI, nVidia, Package, Physics, Tesla K20

November 22, 2016 by hgpu

Celeris: A GPU-accelerated open source software with a Boussinesq-type wave solver for real-time, interactive simulation and visualization

Sasan Tavakkol, Patrick Lynett

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Tags: cfd, DirectX, Finite difference, Fluid dynamics, HLSL, nVidia, nVidia Quadro K600, Package, Physics, Rendering, Visualization

November 22, 2016 by hgpu

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

Richard Membarth, Oliver Reiche, Frank Hannig, Jurgen Teich, Mario Korner, Wieland Eckert

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Tags: Algorithms, AMD Radeon R9 290X, ARM, ATI, Compilers, Computer science, CUDA, Heterogeneous systems, Image processing, Intel Xeon Phi, nVidia, OpenCL, Package, Tesla K20

November 19, 2016 by hgpu

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Pedro Bruel, Marcos Amaris, Alfredo Goldman

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 980, Package, Performance, Tesla K40

November 16, 2016 by hgpu

PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks

Dong Wang, Jianjing An, Ke Xu

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Tags: CNN, Computer science, Deep learning, DSP, FPGA, Hardware Architecture, Neural networks, OpenCL, Package

November 10, 2016 by hgpu

Tamp: A Library for Compact Deep Neural Networks with Structured Matrices

Bingchen Gong, Brendan Jou, Felix Yu, Shih-Fu Chang

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Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, TensorFlow

November 8, 2016 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

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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

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high performance computing on graphics processing units: hgpu.org

Packages

OpenACC cache Directive: Opportunities and Optimizations

A Real-time Single Pulse Detection Algorithm for GPUs

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

Fast and Energy-Efficient CNN Inference on IoT Devices

A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications

GPU-accelerated Red Blood Cells Simulations with Transport Dissipative Particle Dynamics

Celeris: A GPU-accelerated open source software with a Boussinesq-type wave solver for real-time, interactive simulation and visualization

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks

Tamp: A Library for Compact Deep Neural Networks with Structured Matrices

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

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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

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