high performance computing on graphics processing units: hgpu.org

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Automatic Resource-Constrained Static Task Parallelization

Dounia Khaldi

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Tags: Algorithms, Code generation, Computer science, MPI, OpenCL, Task scheduling, Thesis

January 28, 2014 by hgpu

A Dynamic Offload Scheduler for spatial multitasking on Intel Xeon Phi Coprocessor

Takamichi Miyamoto, Kazuhisa Ishizaka, Takeo Hosomi

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Tags: Computer science, Intel Xeon Phi, Task scheduling

January 26, 2014 by hgpu

Multi-GPU Load Balancing for In-Situ Simulation and Visualization

Yong Cao, Robert Hagan

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Tags: Computer science, nVidia, nVidia GeForce GTX 295, Rendering, Task scheduling, Visualization

December 27, 2013 by hgpu

Adaptive Task Size Control on High Level Programming for GPU/CPU Work Sharing

Tetsuya Odajima, Taisuke Boku, Mitsuhisa Sato, Toshihiro Hanawa, Yuetsu Kodama, Raymond Namyst, Samuel Thibault, Olivier Aumage

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Package, Performance, Task scheduling, Tesla M2090

December 25, 2013 by hgpu

Towards global composition of performance-aware components for GPU-based systems

Usman Dastgeer, Christoph Kessler

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Tags: Computer science, CUDA, Flow analysis, Heterogeneous systems, nVidia, Task scheduling, Tesla C2050

December 20, 2013 by hgpu

Data Structures for Task-based Priority Scheduling

Martin Wimmer, Daniel Cederman, Francesco Versaci, Jesper Larsson Traff, Philippas Tsigas

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Tags: Algorithms, Computer science, Intel Xeon Phi, Package, Task scheduling

December 17, 2013 by hgpu

Experience with Intel’s Many Integrated Core architecture in ATLAS software

S. Fleischmann, S. Kama, W. Lavrijsen, M. Neumann, R. Vitillo

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Tags: Computer science, High Energy Physics - Experiment, Intel Phi, Intel Xeon Phi, MPI, Physics, Task scheduling

November 21, 2013 by hgpu

Comparative Performance Analysis of Intel Xeon Phi, GPU, and CPU

George Teodoro, Tahsin Kurc, Jun Kong, Lee Cooper, Joel Saltz

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Tags: Computer science, CUDA, Intel Phi, Intel Xeon Phi, Microscopy, nVidia, OpenMP, Performance, Task scheduling, Tesla K20

November 6, 2013 by hgpu

Batch Method for Efficient Resource Sharing in Real-time Multi-GPU Systems

Uri Verner, Avi Mendelson, Assaf Schuster

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Tags: Computer science, CUDA, nVidia, OpenCL, Performance, Task scheduling, Tesla C2050, Tesla K10

November 4, 2013 by hgpu

Parallel CPU and GPU computations to solve the job shop scheduling problem with blocking

Abdelhakim AitZai, Adel Dabah, Mourad Boudhar

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Tags: Computer science, CUDA, Graph theory, nVidia, nVidia Quadro 2000, Task scheduling

November 3, 2013 by hgpu

Optimization of the HEFT algorithm for a CPU-GPU environment

Karan R. Shetti, Suhaib A. Fahmy, Timo Bretschneider

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Tags: Algorithms, Computer science, Heterogeneous systems, Task scheduling

October 21, 2013 by hgpu

Early Experiences in Running Many-Task Computing Workloads on GPGPUs

Scott J. Krieder, Benjamin Grimmer, Ioan Raicu

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Tags: Computer science, CUDA, nVidia, Task scheduling

October 19, 2013 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Automatic Resource-Constrained Static Task Parallelization

A Dynamic Offload Scheduler for spatial multitasking on Intel Xeon Phi Coprocessor

Multi-GPU Load Balancing for In-Situ Simulation and Visualization

Adaptive Task Size Control on High Level Programming for GPU/CPU Work Sharing

Towards global composition of performance-aware components for GPU-based systems

Data Structures for Task-based Priority Scheduling

Experience with Intel’s Many Integrated Core architecture in ATLAS software

Comparative Performance Analysis of Intel Xeon Phi, GPU, and CPU

Batch Method for Efficient Resource Sharing in Real-time Multi-GPU Systems

Parallel CPU and GPU computations to solve the job shop scheduling problem with blocking

Optimization of the HEFT algorithm for a CPU-GPU environment

Early Experiences in Running Many-Task Computing Workloads on GPGPUs

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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