Erietta Liarou

Cette personne n’est plus à l’EPFL

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Publications associées (5)

How to Stop Under-Utilization and Love Multicores

Anastasia Ailamaki, Pinar Tözün, Danica Porobic, Iraklis Psaroudakis, Erietta Liarou

Hardware trends oblige software to overcome three major challenges against systems scalability: (1) taking advantage of the implicit/vertical parallelism within a core that is enabled through the aggressive micro-architectural features, (2) exploiting the ...

2015

ATraPos: Adaptive Transaction Processing on Hardware Islands

Anastasia Ailamaki, Pinar Tözün, Danica Porobic, Erietta Liarou

Nowadays, high-performance transaction processing applications increasingly run on multisocket multicore servers. Such architectures exhibit non-uniform memory access latency as well as non-uniform thread communication costs. Unfortunately, traditional sha ...

2014

Dynamic Fine-Grained Scheduling for Energy-Efficient Main-Memory Queries

Anastasia Ailamaki, Pinar Tözün, Danica Porobic, Iraklis Psaroudakis, Erietta Liarou

Power and cooling costs are some of the highest costs in data centers today, which make improvement in energy efficiency crucial. Energy efficiency is also a major design point for chips that power whole ranges of computing devices. One important goal in t ...

2014

How to Stop Under-Utilization and Love Multicores

Anastasia Ailamaki, Pinar Tözün, Danica Porobic, Iraklis Psaroudakis, Erietta Liarou

Designing scalable transaction processing systems on modern hardware has been a challenge for almost a decade. Hardware trends oblige software to overcome three major challenges against systems scalability: (1) Exploiting the abundant thread-level parallel ...

2014

DiNoDB: Efficient Large-Scale Raw Data Analytics

Marko Vukolic, Anastasia Ailamaki, Ioannis Alagiannis, Erietta Liarou, Yongchao Tian

Modern big data workflows, found in e.g., machine learning use cases, often involve iterations of cycles of batch analytics and interactive analytics on temporary data. Whereas batch analytics solutions for large volumes of raw data are well established (e ...

2014

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