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Characterizing Deep-Learning I/O Workloads in TensorFlow
KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).ORCID-id: 0000-0003-0639-0639
KTH, Skolan för elektroteknik och datavetenskap (EECS), Beräkningsvetenskap och beräkningsteknik (CST).
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2018 (Engelska)Ingår i: Proceedings of PDSW-DISCS 2018: 3rd Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis, Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 54-63Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The performance of Deep-Learning (DL) computing frameworks rely on the rformance of data ingestion and checkpointing. In fact, during the aining, a considerable high number of relatively small files are first aded and pre-processed on CPUs and then moved to accelerator for mputation. In addition, checkpointing and restart operations are rried out to allow DL computing frameworks to restart quickly from a eckpoint. Because of this, I/O affects the performance of DL plications. this work, we characterize the I/O performance and scaling of nsorFlow, an open-source programming framework developed by Google and ecifically designed for solving DL problems. To measure TensorFlow I/O rformance, we first design a micro-benchmark to measure TensorFlow ads, and then use a TensorFlow mini-application based on AlexNet to asure the performance cost of I/O and checkpointing in TensorFlow. To prove the checkpointing performance, we design and implement a burst ffer. find that increasing the number of threads increases TensorFlow ndwidth by a maximum of 2.3 x and 7.8 x on our benchmark environments. e use of the tensorFlow prefetcher results in a complete overlap of mputation on accelerator and input pipeline on CPU eliminating the fective cost of I/O on the overall performance. The use of a burst ffer to checkpoint to a fast small capacity storage and copy ynchronously the checkpoints to a slower large capacity storage sulted in a performance improvement of 2.6x with respect to eckpointing directly to slower storage on our benchmark environment.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2018. s. 54-63
Nyckelord [en]
Parallel I/O, Input Pipeline, Deep Learning, TensorFlow
Nationell ämneskategori
Datorteknik
Identifikatorer
URN: urn:nbn:se:kth:diva-248377DOI: 10.1109/PDSW-DISCS.2018.00011ISI: 000462205000006Scopus ID: 2-s2.0-85063062239OAI: oai:DiVA.org:kth-248377DiVA, id: diva2:1302569
Konferens
3rd IEEE/ACM Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems, PDSW-DISCS 2018; Dallas; United States; 12 November 2018
Anmärkning

QC 20190405

Tillgänglig från: 2019-04-05 Skapad: 2019-04-05 Senast uppdaterad: 2019-04-05Bibliografiskt granskad

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Markidis, StefanoSishtla, Chaitanya PrasadLaure, Erwin

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Chien, Steven W. D.Markidis, StefanoSishtla, Chaitanya PrasadHerman, PawelLaure, Erwin
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Beräkningsvetenskap och beräkningsteknik (CST)Parallelldatorcentrum, PDC
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