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HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing

Yi-Hsiang Lai, Yuze Chi, Yuwei Hu, Jie Wang, Cody Hao Yu, Yuan Zhou, Jason Cong, Zhiru Zhang

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Tags: Algorithms, Compilers, Computer science, FPGA, Heterogeneous systems, HLS, OpenCL, Python

January 6, 2019 by hgpu

HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines

Periklis Chrysogelos, Manos Karpathiotakis, Raja Appuswamy, Anastasia Ailamaki

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Tags: Compilers, Computer science, CUDA, Databases, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080

January 6, 2019 by hgpu

Towards Automatic Transformation of Legacy Scientific Code into OpenCL for Optimal Performance on FPGAs

Wim Vanderbauwhede, Syed Waqar Nabi

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Tags: Compilers, Computer science, Fortran, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenACC, OpenCL, OpenMP, Package

January 6, 2019 by hgpu

Clacc: Translating OpenACC to OpenMP in Clang

Joel E. Denny, Seyong Lee, Jeffrey S. Vetter

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Tags: Compilers, Computer science, Heterogeneous systems, LLVM, nVidia, nVidia GeForce GTX 1050, OpenACC, OpenMP

December 9, 2018 by hgpu

Automatic Mapping for OpenCL-Programs on CPU/GPU Heterogeneous Platforms

Konrad Moren, Diana Gohringer

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Tags: AMD Radeon R9 290, ATI, Compilers, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 780 Ti, OpenCL

October 28, 2018 by hgpu

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: Compilers, Computer science, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX Titan X, Optimization, Package, performance portability

October 13, 2018 by hgpu

HSTREAM: A directive-based language extension for heterogeneous stream computing

Suejb Memeti, Sabri Pllana

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Tags: Benchmarking, Compilers, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Quadro P 4000, OpenMP

October 6, 2018 by hgpu

Performance Evaluation of OpenMP’s Target Construct on GPUs – Exploring Compiler Optimizations

Akihiro Hayashi, Jun Shirako, Etorre Tiotto, Robert Ho, Vivek Sarkar

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Tags: Compilers, Computer science, CUDA, LLVM, nVidia, OpenMP, Performance, Tesla K80, Tesla P100

August 26, 2018 by hgpu

Spatial: A Language and Compiler for Application Accelerators

David Koeplinger, Matthew Feldman, Raghu Prabhakar, Yaqi Zhang, Stefan Hadjis, Ruben Fiszel, Tian Zhao, Luigi Nardi, Ardavan Pedram, Christos Kozyrakis, Kunle Olukotun

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Tags: Compilers, Computer science, Deep learning, DSL, FPGA, HLS, Machine learning, Package, Scala

July 21, 2018 by hgpu

Application of Deep-Learning to Compiler-Based Graphs

Tristan Vanderbruggen

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Tags: Compilers, Computer science, Deep learning, Graph theory, Machine learning, Neural networks, nVidia, nVidia GRID K520, OpenCL, Thesis

July 7, 2018 by hgpu

Compiler Fuzzing through Deep Learning

Chris Cummins, Pavlos Petoumenos, Hugh Leather, Alastair Murray

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Tags: Compilers, Computer science, Deep learning, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 780, OpenCL, Package

July 1, 2018 by hgpu

DeepSmith: Compiler Fuzzing through Deep Learning

Chris Cummins, Pavlos Petoumenos, Alastair Murray, Hugh Leather

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Tags: Compilers, Computer science, Deep learning, nVidia, OpenCL, Package, Tesla K40

June 24, 2018 by hgpu

HipKittens: Fast and Furious AMD Kernels

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

mt4g: Memory Topology 4 GPUs

MT4G: A Tool for Reliable Auto-Discovery of NVIDIA and AMD GPU Compute and Memory Topologies

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

A High-Throughput GPU Framework for Adaptive Lossless Compression of Floating-Point Data

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

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