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HeSP: a simulation framework for solving the task scheduling-partitioning problem on heterogeneous architectures

Anton Rey, Francisco D. Igual, Manuel Prieto-Matias

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Tags: Algorithms, ARM, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, OpenCL, Task scheduling

February 19, 2016 by hgpu

Automatic and portable mapping of data parallel programs to OpenCL for GPU-based heterogeneous systems

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

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Tags: ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 580, OpenCL, OpenMP, Performance

February 19, 2016 by hgpu

LN-Annote: An Alternative Approach to Information Extraction from Emails using Locally-Customized Named-Entity Recognition

YoungHoon Jung, Karl Stratos, Luca P. Carloni

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Tags: Algorithms, ARM, Cloud, Computer science, Machine learning, Neural networks, NLP, OpenCL

February 19, 2016 by hgpu

Deep Feature-based Face Detection on Mobile Devices

Sayantan Sarkar, Vishal M. Patel, Rama Chellappa

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Tags: Computer science, Computer vision, Deep learning, nVidia, nVidia GeForce GTX 850 M, OpenCL, RenderScript

February 18, 2016 by hgpu

Speeding Up Reinforcement Learning with Graphics Processing Units

Jorn H. Postma

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Tags: Algorithms, Computer science, KD-tree, Machine learning, nVidia, OpenCL, Tesla C2075, Thesis

February 18, 2016 by hgpu

Deep Learning on FPGAs: Past, Present, and Future

Griffin Lacey, Graham W. Taylor, Shawki Areibi

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Tags: Artificial intelligence, Computer science, Deep learning, FPGA, Machine learning, OpenCL, Overview

February 17, 2016 by hgpu

Finding, Measuring, and Reducing Inefficiencies in Contemporary Computer Systems

Melanie Rae Kambadur

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Tags: Computer science, Intel HD 4600, Intel HD Graphics 4000, LLVM, OpenCL, Package, Performance, Thesis

February 16, 2016 by hgpu

SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures

Alexandros Koliousis, Matthias Weidlich, Raul Castro Fernandez, Alexander L. Wolf, Paolo Costa, Peter Pietzuch

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Tags: Computer science, Databases, Heterogeneous systems, nVidia, nVidia Quadro K5200, OpenCL

February 16, 2016 by hgpu

CaffeLink: Mathematica binding for Caffe Deep Learning Framework

Martin Kerhart, Jan Drchal

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

February 16, 2016 by hgpu

Towards Improving Programmability of Heterogeneous Parallel Architectures

Michele Scandale

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Tags: Compilers, Computer science, Heterogeneous systems, LLVM, OpenCL, Thesis

February 16, 2016 by hgpu

Parallel and Scalable Sparse Basic Linear Algebra Subprograms

Weifeng Liu

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Tags: Algorithms, AMD Radeon R9 290X, ATI, Benchmarking, Computer science, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan Black, OpenCL, Sparse matrix, Thesis

February 16, 2016 by hgpu

Writing a performance-portable matrix multiplication

Jorge F. Fabeiro, Diego Andrade, Basilio B. Fraguela

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Tags: AMD FirePro S9150, ATI, Code generation, Computer science, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Performance, performance portability, Tesla K20

February 11, 2016 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

HeSP: a simulation framework for solving the task scheduling-partitioning problem on heterogeneous architectures

Automatic and portable mapping of data parallel programs to OpenCL for GPU-based heterogeneous systems

Deep Feature-based Face Detection on Mobile Devices

Speeding Up Reinforcement Learning with Graphics Processing Units

Deep Learning on FPGAs: Past, Present, and Future

Finding, Measuring, and Reducing Inefficiencies in Contemporary Computer Systems

SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures

CaffeLink: Mathematica binding for Caffe Deep Learning Framework

Towards Improving Programmability of Heterogeneous Parallel Architectures

Parallel and Scalable Sparse Basic Linear Algebra Subprograms

Writing a performance-portable matrix multiplication

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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