Abdelkader Sellami

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Non-uniform memory access (NUMA) architectures pose numerous performance challenges for main-memory column-stores in scaling up analytics on modern multi-socket multi-core servers. A NUMAaware execution engine needs a strategy for data placement and task scheduling that prefers fast local memory accesses over remote memory accesses, and avoids an imbalance(More)
Main-memory column-stores are called to efficiently use modern non-uniform memory access (NUMA) architectures to service concurrent clients on big data. The efficient usage of NUMA architectures depends on the data placement and scheduling strategy of the column-store. Most column-stores choose a static strategy that involves partitioning all data across(More)
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