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Comparing the Performance of Different x86 SIMD Instruction Sets for a Medical Imaging Application on Modern Multi- and Manycore Chips

Johannes Hofmann, Jan Treibig, Georg Hager, Gerhard Wellein

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Tags: Algorithms, Computed tomography, Computer science, CT, CUDA, Image reconstruction, Intel Xeon Phi, Medicine, nVidia, nVidia GeForce GTX 680, Performance

January 30, 2014 by hgpu

A GPU accelerated algorithm for 3D Delaunay triangulation

Thanh-Tung Cao, Ashwin Nanjappa, Mingcen Gao, Tiow-Seng Tan

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Tags: Algorithms, Computational Geometry, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Package

January 30, 2014 by hgpu

A Detailed GPU Cache Model Based on Reuse Distance Theory

Cedric Nugteren, Gert-Jan van den Braak, Henk Corporaal, Henri Bal

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Source codes

Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, nVidia GeForce GTX 470, Package

January 29, 2014 by hgpu

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Gary Macindoe

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, Machine learning, Matrix decomposition, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla S1070, Thesis

January 29, 2014 by hgpu

Consolidating Applications for Energy Efficiency in Heterogeneous Computing Systems

Jing Zhang, Hao Wang, Heshan Lin, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenCL, Task scheduling

January 29, 2014 by hgpu

On the Programmability and Performance of Heterogeneous Platforms

Konstantinos Krommydas, Thomas R.W. Scogland, Wu-chun Feng

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Tags: Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, N-body simulation, nVidia, OpenACC, Optimization, Tesla K20

January 29, 2014 by hgpu

A Performance Criteria for parallel Computation on basis of block size using CUDA Architecture

Ashis Kumar Dash

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Tags: Computer science, CUDA, Data parallelism, Heterogeneous systems, Matrix multiplication, nVidia, nVidia Quadro FX 3700

January 29, 2014 by hgpu

Impact of communication times on mixed CPU/GPU applications scheduling using KAAPI

David Beniamine

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Tags: Computer science, CUDA, nVidia, nVidia Quadro NVS 300, Task scheduling, Tesla C2050, Thesis

January 29, 2014 by hgpu

Scheduling on Manycore and Heterogeneous Graphics Processors

Stanley Tzeng

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenCL, Thesis

January 28, 2014 by hgpu

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

GPU-Qin: A Methodology for Evaluating the Error Resilience of GPGPU Applications

Bo Fang, Karthik Pattabiraman, Matei Ripeanu, Sudhanva Gurumurthi

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Tags: Computer science, CUDA, GPGPU-sim, nVidia

January 28, 2014 by hgpu

Performance-Correctness Challenges in Emerging Heterogeneous Multicore Processors

Vineeth Thamarassery Mekkat

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, nVidia GeForce 8600 GTS, Thesis

January 28, 2014 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

Comparing the Performance of Different x86 SIMD Instruction Sets for a Medical Imaging Application on Modern Multi- and Manycore Chips

A GPU accelerated algorithm for 3D Delaunay triangulation

A Detailed GPU Cache Model Based on Reuse Distance Theory

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Consolidating Applications for Energy Efficiency in Heterogeneous Computing Systems

On the Programmability and Performance of Heterogeneous Platforms

A Performance Criteria for parallel Computation on basis of block size using CUDA Architecture

Impact of communication times on mixed CPU/GPU applications scheduling using KAAPI

Scheduling on Manycore and Heterogeneous Graphics Processors

Automatic Resource-Constrained Static Task Parallelization

GPU-Qin: A Methodology for Evaluating the Error Resilience of GPGPU Applications

Performance-Correctness Challenges in Emerging Heterogeneous Multicore Processors

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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