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Breslow Alexander D
Researcher at Advanced Micro Devices
Publications - 17
Citations - 599
Breslow Alexander D is an academic researcher from Advanced Micro Devices. The author has contributed to research in topics: Set (abstract data type) & Hash function. The author has an hindex of 8, co-authored 17 publications receiving 501 citations. Previous affiliations of Breslow Alexander D include University of California, San Diego.
Papers
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Proceedings ArticleDOI
Bubble-flux: precise online QoS management for increased utilization in warehouse scale computers
TL;DR: B Bubble-Flux is presented, an integrated dynamic interference measurement and online QoS management mechanism to provide accurate QoS control and maximize server utilization.
Journal ArticleDOI
Morton filters: faster, space-efficient cuckoo filters via biasing, compression, and decoupled logical sparsity
TL;DR: This work introduces the Morton filter (MF), a novel AS-MDS that introduces several key improvements to CFs, and typically uses comparable to slightly less space than CFs for the same epsis.
Proceedings Article
Horton tables: fast hash tables for in-memory data-intensive computing
TL;DR: The Horton table is presented, a revamped BCHT that reduces the expected cost of positive and negative lookups to fewer than 1.18 and 1.06 buckets, while still achieving load factors of 95%.
Proceedings ArticleDOI
Enabling fair pricing on HPC systems with node sharing
TL;DR: POPPA is a runtime system that enables fair pricing by delivering precise online interference detection and facilitates the adoption of supercomputers with co-locations and is able to quantify inter-application interference within 4% mean absolute error on a variety of co-located benchmark and real scientific workloads.
Journal ArticleDOI
The case for colocation of high performance computing workloads
Breslow Alexander D,Leo Porter,Ananta Tiwari,Michael A. Laurenzano,Laura Carrington,Dean M. Tullsen,Allan Snavely,Allan Snavely,Allan Snavely +8 more
TL;DR: Job striping is presented, a technique that significantly increases performance over the current allocation mechanism by colocating pairs of jobs from different users on a shared set of nodes and provides a simple set of heuristics for avoiding low performing application pairs.