DEVELOPER TOOLS

Check out this collection of posters on how developers are accelerating their work with the power of GPUs.

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    Accelerating Layout Density Calculation in VLSI Design

    Che-Rung Lee, Associate Professor, National Tsing Hua University

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    An Implementation of Unified Layer performing Convolution and Average Pooling on GPU

    Akihiko Kasagi, researcher, Fujitsu laboratory ltd.

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    Harnessing GPU`s FP16 Arithmetic to Speedup Mixed-Precision Solvers and Achieve 74 Gflops/Watt on Nvidia V100

    Azzam Haidar, Research Scientist, University of Tennessee

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    Tensor Contractions using Optimized Batch GEMM Routines

    Ahmad Ahmad, Research Scientist, University of Tennessee

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    Parallel Quotient Filter Construction: A Study in Non-Associative Scans

    Afton Geil, PhD Student, UC Davis

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    Kokkoskernels: Portable Math and Graph Kernels

    Kyungjoo Kim, Research Scientist, Sandia National Laboratories

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    Relational Model Finding for Hardware-Aware Program Synthesis and Security Verification

    Caroline Trippel, Ph.D. Candidiate, Princeton University

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    A High-performance Computing Platform for Real-time Radio Spectrum Analysis

    Shihao Song, Student, Drexel University

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    Analyzing the Communication Characteristics of GPU Thread Blocks

    Holger Froning, Associate Professor, Heidelberg University

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    Evaluation of a Non-Hydrostatic Ocean Model Implemented on Pascal GPUs with GPUDirect RDMA Transfer

    Takateru Yamagishi, chief, Research Organization for Information Science and Technology

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    GPU Mekong: Simplified Multi-GPU Programming using Automated Partitioning

    Alexander Matz, Research Associate, Heidelberg University

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    Optimising Scattered Memory Access of Uniform Spatial Partitioning

    Robert Chisholm, PhD Student, University of Sheffield

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    The Kokkos Programming Model

    Christian Trott, Principal Member of Staff, Sandia National Laboratories

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    Efficiently Enlarging GPU Memory Capacity with NVM

    Pak Markthub, Student, Tokyo Institute of Technology

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    CS-DTW: Efficient DTW on Huge Data Size in Real-time using Compute Shaders

    Brenan Balbido, Student Bs in Computer Science, California Polytechnic State University San Luis Obispo