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Turbocharging Rack-Scale In-Memory Computing with Scale-Out NUMA

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Web-scale online services mandate fast access to massive quantities of data. In practice, this is accomplished by sharding the datasets across a pool of servers within a datacenter and keeping each shard in the servers’ main memory to avoid long-latency disk I/O. Accesses to non-local shards take place over the datacenter network, incurring communication delays that are 20-1000x greater than accesses to local memory. In this talk, I will introduce Scale-Out NUMA —a rack-scale architecture with an RDMA -inspired programming model that eliminates chief latency overheads of existing networking technologies and reduces the remote memory access latency to a small factor of local DRAM . I will overview key features of Scale-Out NUMA and will describe how it can bridge the semantic gap between software and hardware through integrated support for atomic object reads.

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