high performance computing on graphics processing units: hgpu.org

Ian Karlin, Abhinav Bhatele, Jeff Keasler, Bradford L. Chamberlain, Jonathan Cohen, Zachary DeVito, Riyaz Haque, Dan Laney, Edward Luke, Felix Wang, David Richards, Martin Schulz, Charles H. Still

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Tesla M2050

January 13, 2013 by hgpu

Evaluating Reconfigurable Dataflow Computing Using the Himeno Benchmark

Yukinori Sato, Yasushi Inoguchi, Wayne Luk, Tadao Nakamura

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Tags: Benchmarking, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Tesla M2050

January 11, 2013 by hgpu

Parallel Algorithms for Constructing Data Structures for Fast Multipole Methods

Qi Hu, Nail A. Gumerov, Ramani Duraiswami

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Tags: Algorithms, Computer science, CUDA, Fast multipole method, Heterogeneous systems, Mathematical Software, nVidia, nVidia GeForce GTX 480

January 11, 2013 by hgpu

On Graphs, GPUs, and Blind Dating: A Workload to Processor Matchmaking Quest

Abdullah Gharaibeh, Lauro Beltrao Costa, Elizeu Santos-Neto, Matei Ripeanu

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla C2075

January 10, 2013 by hgpu

Portable Mapping of Data Parallel Programs to OpenCL for Heterogeneous Systems

Dominik Grewe, Zheng Wang, Michael F.P. O'Boyle

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Tags: ATI, ATI Radeon HD 7970, Compilers, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 580, OpenCL, Programming Languages

January 8, 2013 by hgpu

Implementation of FDTD-Compatible Green’s Function on Heterogeneous CPU-GPU Parallel Processing System

Tomasz P. Stefanski

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Tags: CUDA, Electrodynamics, FDTD, Finite-difference time-domain, Heterogeneous systems, nVidia, nVidia GeForce GTX 680

January 8, 2013 by hgpu

Interactive Refactoring for GPU Parallelization of Affine Loops

Kostadin Damevski, Madhan Muralimanohar

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Quadro FX 2000

January 7, 2013 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

Developing and Evaluating clOpenCL Applications for Heterogeneous Clusters

The PEPPHER Composition Tool: Performance-Aware Dynamic Composition of Applications for GPU-based Systems

The Fast Multipole Method on the Cell processor

Fast Positron Range Calculation in Heterogeneous Media for 3D PET Reconstruction

Adaptive Dynamic Load Balancing in Heterogeneous Multiple GPUs-CPUs Distributed Setting: Case Study of B&B Tree Search

Exploring Traditional and Emerging Parallel Programming Models using a Proxy Application

Evaluating Reconfigurable Dataflow Computing Using the Himeno Benchmark

Parallel Algorithms for Constructing Data Structures for Fast Multipole Methods

On Graphs, GPUs, and Blind Dating: A Workload to Processor Matchmaking Quest

Portable Mapping of Data Parallel Programs to OpenCL for Heterogeneous Systems

Implementation of FDTD-Compatible Green’s Function on Heterogeneous CPU-GPU Parallel Processing System

Interactive Refactoring for GPU Parallelization of Affine Loops

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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