high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

Syed Tahir Hussain Rizvi, Gianpiero Cabodi, Denis Patti, Gianluca Francini

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Tags: CNN, Computer science, CUDA, Deep learning, Heterogeneous systems, Image processing, Matrix multiplication, Neural networks, nVidia, nVidia Jetson TK1

December 10, 2016 by hgpu

BrainFrame: A heterogeneous accelerator platform for neuron simulations

Georgios Smaragdos, Georgios Chatzikonstantis, Rahul Kukreja, Harrys Sidiropoulos, Dimitrios Rodopoulos, Ioannis Sourdis, Zaid Al-Ars, Christoforos Kachris, Dimitrios Soudris, Chris I. De Zeeuw, Christos Strydis

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Tags: Biophysics, Computer science, Heterogeneous systems, Intel Xeon Phi, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX Titan X, OpenMP

December 10, 2016 by hgpu

A Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling

Marcos Amaris, Raphael Y. de Camargo, Mohamed Dyab, Alfredo Goldman, Denis Trystram

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Tags: Analytical model, Benchmarking, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 970, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan Black, nVidia GeForce GTX Titan X, nVidia Quadro K5200, Package, Performance, Tesla K20, Tesla K40

December 6, 2016 by hgpu

Efficient Kernel Synthesis for Performance Portable Programming

Li-Wen Chang, Izzat El Hajj, Christopher Rodrigues, Juan Gomez-Luna, Wen-mei Hwu

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Tags: Code generation, Computer science, CUDA, Design space exploration, Heterogeneous systems, nVidia, performance portability, Tesla C2050, Tesla K20

November 25, 2016 by hgpu

Processing OLTP Workloads on Hybrid CPU/GPU Systems

Mudit Bhatnagar

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Tags: Computer science, Databases, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GT 640, nVidia GeForce GTX Titan, OpenCL, Thesis

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

Automatic code generation methods applied to numerical linear algebra in high performance computing

M. Ian Masliah

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Tags: Algorithms, Code generation, Computer science, CUDA, Heterogeneous systems, Linear Algebra, Mixed precision, nVidia, nVidia Tegra TX1, Programming techniques, Tesla C2075, Tesla K40, Thesis

November 16, 2016 by hgpu

OpenCL-based optimizations for acceleration of object tracking on FPGAs and GPUs

Amir Momeni, Hamed Tabkhi, Gunar Schirner, David Kaeli

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Tags: Algorithms, Computer science, FPGA, Heterogeneous systems, nVidia, OpenCL, Tesla K20

November 13, 2016 by hgpu

Executing Dynamic Data Rate Actor Networks on OpenCL Platforms

Jani Boutellier, Ilkka Hautala

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Tags: ATI, ATI Radeon HD 7750, Computer science, Heterogeneous systems, OpenCL, Signal processing

November 13, 2016 by hgpu

Hadoopcl2: Motivating the design of a distributed, heterogeneous programming system with machine-learning applications

Max Grossman, Mauricio Breternitz, Vivek Sarkar

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Tags: Algorithms, Computer science, Hadoop, Heterogeneous systems, Machine learning, MapReduce, nVidia, OpenCL, Spark, Tesla M2050

November 13, 2016 by hgpu

HPVM: A Portable Virtual Instruction Set for Heterogeneous Parallel Systems

Prakalp Srivastava, Maria Kotsifakou, Vikram Adve

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Tags: Code generation, Computer science, Data parallelism, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, performance portability

November 5, 2016 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

BrainFrame: A heterogeneous accelerator platform for neuron simulations

A Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling

Efficient Kernel Synthesis for Performance Portable Programming

Processing OLTP Workloads on Hybrid CPU/GPU Systems

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

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Automatic code generation methods applied to numerical linear algebra in high performance computing

OpenCL-based optimizations for acceleration of object tracking on FPGAs and GPUs

Executing Dynamic Data Rate Actor Networks on OpenCL Platforms

Hadoopcl2: Motivating the design of a distributed, heterogeneous programming system with machine-learning applications

HPVM: A Portable Virtual Instruction Set for Heterogeneous Parallel Systems

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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