Efficient Translation of Algorithmic Kernels on Large-Scale Multi-cores

Pande, Amit; Zambreno, Joseph

doi:10.1109/cse.2009.494

Cited by 4 publications

(2 citation statements)

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“…There have been several studies that have evaluated the performance of network I/O in multicore systems [11]- [14]. A major improvement which is enabled by default in almost all the current kernels is NAPI [3], [15].…”

Section: Related Workmentioning

confidence: 99%

Improving network performance on multicore systems: Impact of core affinities on high throughput flows

Hanford

Ahuja

Farrens

et al. 2016

Future Generation Computer Systems

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Network throughput is scaling-up to higher data rates while end-system processors are scaling-out to multiple cores. In order to optimize high speed data transfer into multicore end-systems, techniques such as network adapter offloads and performance tuning have received a great deal of attention. Furthermore, several methods of multi-threading the network receive process have been proposed. However, thus far attention has been focused on how to set the tuning parameters and which offloads to select for higher performance, and little has been done to understand why the various parameter settings do (or do not) work. In this paper, we build on previous research to track down the sources of the end-system bottleneck for highspeed TCP flows. We define protocol processing efficiency to be the amount of system resources (such as CPU and cache) used per unit of achieved throughput (in Gbps). The amount of various system resources consumed are measured using low-level system event counters. In a multicore end-system, affinitization, or core binding, is the decision regarding how the various tasks of network receive process including interrupt, network, and application processing are assigned to the different processor cores. We conclude that affinitization has a significant impact on protocol processing efficiency, and that the performance bottleneck of the network receive process changes significantly with different affinitization.

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Section: Related Workmentioning

confidence: 99%

Improving network performance on multicore systems: Impact of core affinities on high throughput flows

Hanford

Ahuja

Farrens

et al. 2016

Future Generation Computer Systems

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“…Internet characterization using analytical modeling, simulation, and empirical measurements has received a lot of attention.Many researchers have modeled high speed traffic (such as video) on wireless networks [21,8] or using dedicated custom interconnects in multicores [22]. But very few studies have investigated the impact of high speed traffic on end-hosts, especially when they are connected using dedicated networks.…”

Section: Related Workmentioning

confidence: 99%

Characterizing the impact of end-system affinities on the end-to-end performance of high-speed flows

Hanford

Ahuja

Balman

et al. 2013

Proceedings of the Third International Workshop on Network-Aware Data Management

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Multi-core end-systems use Receive Side Scaling (RSS) to parallelize protocol processing. RSS uses a hash function on the standard flow descriptors and an indirection table to assign incoming packets to receive queues which are pinned to specific cores. This ensures flow affinity in that the interrupt processing of all packets belonging to a specific flow is processed by the same core. A key limitation of standard RSS is that it does not consider the application process that consumes the incoming data in determining the flow affinity. In this paper, we carry out a detailed experimental analysis of the performance impact of the application affinity in a 40 Gbps testbed network with a dual hexa-core endsystem. We show, contrary to conventional wisdom, that when the application process and the flow are affinitized to the same core, the performance (measured in terms of endto-end TCP throughput) is significantly lower than the line rate. Near line rate performance is observed when the flow and the application process are affinitized to different cores belonging to the same socket. Furthermore, affinitizing the application and the flow to cores on different sockets results in significantly lower throughput than the line rate. These results arise due to the memory bottleneck, which is demonstrated using preliminary correlational data on the cache hit rate in the core that services the application process.

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