NVIDIA Tesla: A Unified Graphics and Computing Architecture

By Erik Lindholm, John R. Nickolls, Stuart F. Oberman, John Montrym. 2008

1,563 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture, ML Systems & Hardware

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Citations in the corpus

Billion-Scale Similarity Search with GPUs (2017, background): “We then focus on one of the less GPU-compliant parts involved in similarity search, namely the k -selection, and discuss the literature and challenges.”
The Devil is in the Tails: Fine-grained Classification in the Wild (2017, background): “…29], (b) the design and construction of large and well annotated datasets [8, 11, 13, 30] supplying researchers with a sufﬁcient amount of data to train learning-based algorithms, and (c) the availability of inexpensive and ever more powerful computers, such as GPUs [33], for algorithm training.”
A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation (2009, neutral): “linear filtering, local normalization) were created for both the IBM Cell Processor (PlayStation 3), and for NVIDIA graphics processing units (GPUs) using the Tesla Architecture and the CUDA programming model [25].”
A quantitative study of irregular programs on GPUs (2012, background): “These codes often have high computational demands, exhibit extensive data parallelism, access memory in a streaming fashion, and require little synchronization [30].”
A performance study of general-purpose applications on graphics processors using CUDA (2008, background): “[14] provide a nice description of contemporary NVIDIA GPU architectures.”
PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation (2009, background): “Graphics Processing Units (GPUs) [6, 28, 44] promise tremendous advantages in throughput over conventional processor architectures, ideally resulting in a large reduction of execution time for suitable compute-or bandwidth-bound algorithms.”
Designing efficient sorting algorithms for manycore GPUs (2009, methodology): “Current NVIDIA GPUs, for example, contain up to 240 scalar processing elements per chip [9], and in contrast to earlier generations of GPUs, they can be programmed directly in C using CUDA [10], [11].”
HyGCN: A GCN Accelerator with Hybrid Architecture (2020, background): “GPUs are inherently optimized for compute-intensive workloads with regular execution pattern [29] such as neural networks, but handling the Aggregation phase with irregular memory accesses suffers from low efficiency.”
Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking (2018, background): “Every year, novel NVIDIA R © GPU designs are introduced [1, 2, 3, 4, 5, 6].”
Nodal discontinuous Galerkin methods on graphics processors (2009, neutral): “Nowadays, compute abstractions are becoming less encumbered by their graphics heritage [17, 18].”
Ambit: In-Memory Accelerator for Bulk Bitwise Operations Using Commodity DRAM Technology (2017, neutral): “The GTX 745 contains 3 streaming multi-processors, each with 128 CUDA cores [77], and one 128-bit DDR3-1800 channel.”
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (2019, background): “There is a rapidly growing literature on methods to try to address these areas directly [3,17,21,27,31,46,52,67].”
Implementing sparse matrix-vector multiplication on throughput-oriented processors (2009, methodology): “Our SpMV kernels are designed to be run on throughput-oriented architectures in general and GPUs supporting the CUDA parallel computing architecture [15, 16] in particular.”
Neural networks and neuroscience-inspired computer vision. (2014, background): “While an advanced GPU might have thousands of processing cores that operate on data in parallel [9], the brain has billions of neurons operating simultaneously.”