Parallelization of large vector similarity computations in a hybrid CPU+GPU environment

Author:

Czarnul PawełORCID

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Reference27 articles.

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2. Amodei D, Anubhai R, Battenberg E, Case C, Casper J, Catanzaro B, Chen J, Chrzanowski M, Coates A, Diamos G, Elsen E, Engel J, Fan L, Fougner C, Hannun AY, Jun B, Han T, LeGresley P, Li X, Lin L, Narang S, Ng AY, Ozair S, Prenger R, Qian S, Raiman J, Satheesh S, Seetapun D, Sengupta S, Wang C, Wang Y, Wang Z, Xiao B, Xie Y, Yogatama D, Zhan J, Zhu Z (2016) Deep speech 2 :end-to-end speech recognition in english and mandarin. In: Balcan M, Weinberger KQ (eds) Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, 19–24 June 2016, JMLR Workshop and Conference Proceedings, vol 48, pp 173–182. JMLR.org. http://jmlr.org/proceedings/papers/v48/amodei16.html

3. Awekar A (2009) Samatova NF Fast matching for all pairs similarity search. IEEE/WIC/ACM Int Conf Web Intell Intell Agent Technol 1:295–300. https://doi.org/10.1109/WI-IAT.2009.52

4. Bayardo RJ, Ma Y, Srikant R (2007) Scaling up all pairs similarity search. In: Proceedings of the 16th International Conference on World Wide Web, WWW ’07, pp 131–140. ACM, New York, NY, USA. https://doi.org/10.1145/1242572.1242591

5. Czarnul P (2016) Benchmarking performance of a hybrid intel xeon/xeon phi system for parallel computation of similarity measures between large vectors. Int J Parallel Program. https://doi.org/10.1007/s10766-016-0455-0

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