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Network bound applications, like a database server executing OLTP queries ora caching server storing objects for a dynamic web applications, are essentialservices that consumers and businesses use daily. These services run on a largedatacenters and are required to meet predefined Service Level Objectives (SLO),or latency targets, with high probability. Thus, efficient datacenterapplications should optimize their execution in terms of power and performance.However, to support large scale data storage, these workloads make heavy use ofpointer connected data structures (e.g., hash table, large fan-out tree, trie)and exhibit poor instruction and memory level parallelism. Our experiments showthat due to long memory access latency, these workloads occupy processorresources (e.g., ROB entries, RS buffers, LS queue entries etc.) for aprolonged period of time that delay the processing of subsequent requests.Delayed execution not only increases request processing latency, but alsoseverely effects an application throughput and power-efficiency. To overcomethis limitation, we present CARGO, a novel mechanism to overlap queuing latencyand request processing by executing select instructions on an applicationcritical path at the network interface card (NIC) while requests wait forprocessor resources to become available. Our mechanism dynamically identifiesthe critical instructions and includes the register state needed to compute thelong latency memory accesses. This context-augmented critical region is oftenexecuted at the NIC well before execution begins at the core, effectivelyprefetching the data ahead of time. Across a variety of interactive datacenterapplications, our proposal improves latency, throughput, and power efficiencyby 2.7X, 2.7X, and 1.5X, respectively, while incurring a modest amount storageoverhead.

CARGO : Context Augmented Critical Region Offload for Network-bound datacenter Workloads