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A Survey Of Architectural Approaches for Managing Embedded DRAM and Non-volatile On-chip Caches

Sparsh Mittal; Jeffrey S. Vetter; Dong Li

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Tags: cache, Compiler, cpu, Energy-efficient computing, GPU, Hardware Architecture, Memory model

January 12, 2015 by sparsh0mittal

A Survey Of Techniques for Managing and Leveraging Caches in GPUs

Sparsh Mittal

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Tags: GPGPU, GPGPU architecture, GPU, Hardware, Hardware Architecture

November 18, 2014 by sparsh0mittal

Understanding the ISA impact on GPU Architecture

Mayank Vinodbhai Kothiya

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, PTX, Tesla, Thesis

July 28, 2014 by hgpu

Fine-grain Task Aggregation and Coordination on GPUs

Marc S. Orr, Bradford M. Beckmann, Steven K. Reinhardt, David A. Wood

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Tags: Computer science, GPGPU-sim, Hardware Architecture, OpenCL, Performance

May 10, 2014 by hgpu

Architectural Support for Virtual Memory in GPUs

Bharath Subramanian Pichai

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Tags: Computer science, Hardware Architecture, Heterogeneous systems, Memory, Thesis

April 19, 2014 by hgpu

A Survey of Methods For Analyzing and Improving GPU Energy Efficiency

Sparsh Mittal, Jeffrey S. Vetter

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Tags: comparison, Computer science, cpu, CUDA, Energy-efficient computing, FPGA, Hardware Architecture, nVidia

April 18, 2014 by sparsh0mittal

Precision-Aware Soft Error Protection for GPUs

David J. Palframan, Nam Sung Kim, Mikko H. Lipasti

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Tags: Computer science, GPGPU-sim, Hardware Architecture, nVidia, nVidia Quadro FX 5800

February 25, 2014 by hgpu

A Scalable Multi-Path Microarchitecture for Efficient GPU Control Flow

Ahmed ElTantawy, Jessica Wenjie Ma, Mike O'Connor, Tor M. Aamodt

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Tags: Computer science, Hardware Architecture, Performance

February 9, 2014 by hgpu

A Detailed GPU Cache Model Based on Reuse Distance Theory

Cedric Nugteren, Gert-Jan van den Braak, Henk Corporaal, Henri Bal

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, nVidia GeForce GTX 470, Package

January 29, 2014 by hgpu

Performance-Correctness Challenges in Emerging Heterogeneous Multicore Processors

Vineeth Thamarassery Mekkat

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, nVidia GeForce 8600 GTS, Thesis

January 28, 2014 by hgpu

Supporting x86-64 Address Translation for 100s of GPU Lanes

Jason Power, Mark D. Hill, David A. Wood

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Tags: Benchmarking, Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia

January 18, 2014 by hgpu

Architectural improvements and 28 nm FPGA implementation of the APEnet+ 3D Torus network for hybrid HPC systems

Roberto Ammendola, Andrea Biagioni, Ottorino Frezza, Francesca Lo Cicero, Pier Stanislao Paolucci, Alessandro Lonardo, Davide Rossetti, Francesco Simula, Laura Tosoratto, Piero Vicini

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Tags: Computer science, FPGA, GPU cluster, Hardware Architecture, nVidia

November 8, 2013 by hgpu

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

Automated Instruction Encoding Synthesis for Modern GPU ISA Compression

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

EinsummableV3 - graph-based computation with tensor operations support

Every Kernel Is a Join: Automatic Multi-GPU Parallelism for AI Computations in Einsummable

MaxKernel: Intelligent TPU Kernel Engineering Agents

MaxKernel: Agentic Kernel Generation for TPUs

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