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GPGPU Based Parallelized Client-Server Framework for Providing High Performance Computation Support

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Poorna Banerjee, Amit Dave

Distributed, Parallel, and Cluster Computing · Submitted 21 May 2015

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This paper describes a client-server framework in which users can choose a processing task and submit large data-sets for processing to a remote GPGPU and receive the results back, using well defined interfaces.

how it is stated · asserted 1.00 read the record →

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arXiv ID1505.05655 (version 1)
Pith IDP77VZCQW, Pith's permanent record of this paper and its review
CitationAmit Dave, Poorna Banerjee. "GPGPU Based Parallelized Client-Server Framework for Providing High Performance Computation Support." arXiv:1505.05655 (2015). https://pith.science/paper/P77VZCQW
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,549 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.06 · human participants 0.04 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.04 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Parallel data processing has become indispensable for processing applications involving huge data sets. This brings into focus the Graphics Processing Units (GPUs) which emphasize on many-core computing. With the advent of General Purpose GPUs (GPGPU), applications not directly associated with graphics operations can also harness the computation capabilities of GPUs. Hence, it would be beneficial if the computing capabilities of a given GPGPU could be task optimized and made available. This paper describes a client-server framework in which users can choose a processing task and submit large data-sets for processing to a remote GPGPU and receive the results back, using well defined interfaces. The framework provides extensibility in terms of the number and type of tasks that the client can choose or submit for processing at the remote GPGPU server machine, with complete transparency to the underlying hardware and operating systems. Parallelization of user-submitted tasks on the GPGPU has been achieved using NVIDIA Compute Unified Device Architecture (CUDA).

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Parallel data processing has become indispensable for processing applications involving huge data sets. background 1.00 0.04 0.03 asserted 1.00
s1 This brings into focus the Graphics Processing Units (GPUs) which emphasize on many-core computing. background 1.00 0.04 0.02 asserted 1.00
s2 With the advent of General Purpose GPUs (GPGPU), applications not directly associated with graphics operations can also harness the computation capabilities of GPUs. background 1.00 0.05 0.04 asserted 0.88
s3 Hence, it would be beneficial if the computing capabilities of a given GPGPU could be task optimized and made available. background 0.50 0.06 0.07 hedged possibility 0.89
s4 This paper describes a client-server framework in which users can choose a processing task and submit large data-sets for processing to a remote GPGPU and receive the results back, using well defined interfaces. contribution 0.93 0.83 0.23 asserted 1.00
s5 The framework provides extensibility in terms of the number and type of tasks that the client can choose or submit for processing at the remote GPGPU server machine, with complete transparency to the underlying hardware and operating systems. contribution 0.87 0.81 0.15 asserted 1.00
s6 Parallelization of user-submitted tasks on the GPGPU has been achieved using NVIDIA Compute Unified Device Architecture (CUDA). method 1.00 0.87 0.09 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.62.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Parallel data processing has become indispensable for processing applications involving huge data sets. This brings into focus the Graphics Processing Units (GPUs) which emphasize on many-core computing. With the advent of General Purpose GPUs (GPGPU), applications not directly associated with graphics operations can also harness the computation capabilities of GPUs. Hence, it would be beneficial if the computing capabilities of a given GPGPU could be task optimized and made available. This paper describes a client-server framework in which users can choose a processing task and submit large data-sets for processing to a remote GPGPU and receive the results back, using well defined interfaces. The framework provides extensibility in terms of the number and type of tasks that the client can choose or submit for processing at the remote GPGPU server machine, with complete transparency to the underlying hardware and operating systems. Parallelization of user-submitted tasks on the GPGPU has been achieved using NVIDIA Compute Unified Device Architecture (CUDA).

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