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Don't cry over spilled records: Memory elasticity of data-parallel applications and its application to cluster scheduling

Publications associées (32)

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Manycore clique enumeration with fast set intersections

Paolo Ienne, Kubilay Atasu, Radu Ioan Stoica, Jovan Blanusa

Listing all maximal cliques of a given graph has important applications in the analysis of social and biological networks. Parallelisation of maximal clique enumeration (MCE) algorithms on modern manycore processors is challenging due to the task-level par ...

ACM2020

Database systems access memory either sequentially or randomly. Contrary to sequential access and despite the extensive efforts of computer architects, compiler writers, and system builders, random access to data larger than the processor cache has been s ...

EPFL2019

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With the spread of cloud services and Internet of Things concept, there is a popularization of machine learning and artificial intelligence based analytics in our everyday life. However, an efficient deployment of these data-intensive services requires per ...

2019

Hybrid, Job-Aware, and Preemptive Datacenter Scheduling

Pamela Isabel Delgado Borda

Scheduling in datacenters is an important, yet challenging problem. Datacenters are composed of a large number, typically tens of thousands, of commodity computers running a variety of data-parallel jobs. The role of the scheduler is to assign cluster reso ...

EPFL2018

Scaling and Resilience in Numerical Algorithms for Exascale Computing

Allan Svejstrup Nielsen

The first Petascale supercomputer, the IBM Roadrunner, went online in 2008. Ten years later, the community is now looking ahead to a new generation of Exascale machines. During the decade that has passed, several hundred Petascale capable machines have bee ...

EPFL2018

Don't cry over spilled records: Memory elasticity of data-parallel applications and its application to cluster scheduling

Willy Zwaenepoel, Calin Iorgulescu, Syed Mohammad Aunn Raza, Florin Dinu, Wajih Ul Hassan

Understanding the performance of data-parallel workloads when resource-constrained has significant practical importance but unfortunately has received only limited attention. This paper identifies, quantifies and demonstrates memory elasticity, an intrinsi ...

The USENIX Association2017

Job-aware Scheduling in Eagle: Divide and Stick to Your Probes

Willy Zwaenepoel, Pamela Isabel Delgado Borda, Diego Didona, Florin Dinu

We present Eagle, a new hybrid data center scheduler for data-parallel programs. Eagle dynamically divides the nodes of the data center in partitions for the execution of long and short jobs, thereby avoiding head-of-line blocking. Furthermore, it provides ...

ACM2016

Exploiting NVM in Large-scale Graph Analytics

Willy Zwaenepoel, Jasmina Malicevic

Data center applications like graph analytics require servers with ever larger memory capacities. DRAM scaling, how- ever, is not able to match the increasing demands for ca- pacity. Emerging byte-addressable, non-volatile memory technologies (NVM) offer a ...

2015

All computing platforms, from mobile to supercomputers, are becoming more and more heterogeneous and massively parallel. While they can provide higher power efficiency and computation throughput, effective and confident use of these systems always requires ...

EPFL2015

Language Support for Distributed Functional Programming

Software development has taken a fundamental turn. Software today has gone from simple, closed programs running on a single machine, to massively open programs, patching together user experiences byway of responses received via hundreds of network requests ...

EPFL2015