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Automatic Kernel Generation for Volta Tensor Cores

Somashekaracharya G. Bhaskaracharya, Julien Demouth, Vinod Grover

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Tags: Compilers, Computer science, CUBLAS, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia Quadro GV100, Performance, Programming Languages, PTX

June 28, 2020 by hgpu

Autotuning for Automatic Parallelization on Heterogeneous Systems

Philip Pfaffe

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Tags: Auto-Tuning, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenMP, Performance, Tesla P100, Thesis

June 21, 2020 by hgpu

FPGA Based Satisfiability Checking

Rishi Bharadwaj Subramanian

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Tags: Computer science, Electronic design automation, FPGA, Heterogeneous systems, OpenCL, Thesis

June 21, 2020 by hgpu

Ansor: Generating High-Performance Tensor Programs for Deep Learning

Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, Joseph E. Gonzalez, Ion Stoica

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Tags: ARM, Compilers, Computer science, CUDA, Deep learning, Neural networks, nVidia, Optimization, Search strategies, Tesla V100

June 21, 2020 by hgpu

The Rodinia Benchmark Suite in SYCL

Zheming Jin

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Tags: Benchmarking, Computer science, Heterogeneous systems, OpenCL, Package, SYCL

June 14, 2020 by hgpu

A Compiler Infrastructure for Embedded Multicore SoCs

Weihua Sheng

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Tags: Benchmarking, Compilers, Computer science, GLSL, OpenGL, SoC, Software Engineering, Thesis

June 14, 2020 by hgpu

Software Testing – Test Suite Compilation and Execution Optimizations

Panagiotis Stratis

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Tags: ATI, ATI Radeon HD 5450, Benchmarking, Computer science, Heterogeneous systems, Optimization, SYCL, Thesis

June 14, 2020 by hgpu

AutoMat – Automatic Differentiation for Generalized Standard Materials on GPUs

Johannes Blühdorn, Nicolas R. Gauger, Matthias Kabel

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Tags: Computer science, CUDA, FFT, Mathematical Software, nVidia, nVidia Quadro GV100

June 14, 2020 by hgpu

Neural Architecture Search without Training

Joseph Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley

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Tags: Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package

June 14, 2020 by hgpu

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

Jiajian Xiao, Philipp Andelfinger, Wentong Cai, Paul Richmond, Alois Knoll, David Eckhoff

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Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 2080 Ti, OpenCL, Package

June 7, 2020 by hgpu

SOFF: An OpenCL High-Level Synthesis Framework for FPGAs

Gangwon Jo, Heehoon Kim, Jeesoo Lee, Jaejin Lee

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Tags: Compilers, Computer science, FPGA, OpenCL, Package, Performance

June 7, 2020 by hgpu

Investigating Single Precision Floating General Matrix Multiply in Heterogeneous

Steven Harris

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Tags: ASIC, Computer science, FPGA, Heterogeneous systems, Matrix multiplication, OpenCL, Performance, performance portability, Thesis

June 7, 2020 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 Kernel Generation for Volta Tensor Cores

Autotuning for Automatic Parallelization on Heterogeneous Systems

FPGA Based Satisfiability Checking

Ansor: Generating High-Performance Tensor Programs for Deep Learning

The Rodinia Benchmark Suite in SYCL

A Compiler Infrastructure for Embedded Multicore SoCs

Software Testing – Test Suite Compilation and Execution Optimizations

AutoMat – Automatic Differentiation for Generalized Standard Materials on GPUs

Neural Architecture Search without Training

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

SOFF: An OpenCL High-Level Synthesis Framework for FPGAs

Investigating Single Precision Floating General Matrix Multiply in Heterogeneous

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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