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On optimizing machine learning workloads via kernel fusion:
, In:
Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
,
Ashari, Arash
;
Tatikonda, Shirish
;
Boehm, Matthias
... - p. 173-182 , 2015
Link:
https://dl.acm.org/doi/10.1145/2688500.2688521
RT T1 <cTitle>Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming</cTitle>: T1 <Title>On optimizing machine learning workloads via kernel fusion</Title> UL https://suche.suub.uni-bremen.de/peid=acm-2688521&Exemplar=1&LAN=DE A1 Ashari, Arash A1 Tatikonda, Shirish A1 Boehm, Matthias A1 Reinwald, Berthold A1 Campbell, Keith A1 Keenleyside, John A1 Sadayappan, P. PB ACM YR 2015 K1 Dense K1 Fused Kernel K1 GPU K1 Machine Learning K1 Sparse K1 Computing methodologies K1 Concurrent computing methodologies K1 Concurrent programming languages K1 Software and its engineering K1 Software notations and tools K1 General programming languages K1 Language types K1 Artificial intelligence K1 Philosophical/theoretical foundations of artificial intelligence SP 173 OP 182 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/2688500.2688521 DO https://dl.acm.org/doi/10.1145/2688500.2688521 SF ELIB - SuUB Bremen
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