Impact of microarchitectural state reuse on serverless functions

Asheim, Truls; Khan, Tanvir Ahmed; Kasicki, Baris; Kumar, Rakesh

doi:10.1145/3565382.3565879

Cited by 2 publications

(1 citation statement)

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“…These prefetching techniques are complementary to BTB organization and, thus, can be used along with BTB-X. Mitigating L1-I misses: As L1-I misses continue to be a major performance limiter in server applications [12], [25], [46], prior work has proposed both hardware and software mechanisms to mitigate L1-I misses. On the hardware side, state-of-the-art temporal stream prefetchers [19], [20] record the L1-I miss/access history and replay it to discover prefetch candidates.…”

Section: G Analyzing Target Offset Distribution In More Workloadsmentioning

confidence: 99%

A Storage-Effective BTB Organization for Servers

Asheim

Grot

Kumar

2023

2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)

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Many contemporary applications feature multimegabyte instruction footprints that overwhelm the capacity of branch target buffers (BTB) and instruction caches (L1-I), causing frequent front-end stalls that inevitably hurt performance. BTB capacity is crucial for performance as a sufficiently large BTB enables the front-end to accurately resolve the upcoming execution path and steer instruction fetch appropriately. Moreover, it also enables highly effective fetch-directed instruction prefetching that can eliminate a large portion L1-I misses. For these reasons, commercial processors allocate vast amounts of storage capacity to BTBs.This work aims to reduce BTB storage requirements by optimizing the organization of BTB entries. Our key insight is that storing branch target offsets, instead of full or compressed targets, can drastically reduce BTB storage cost as the vast majority of dynamic branches have short offsets requiring just a handful of bits to encode. Based on this insight, we size the ways of a set associative BTB to hold different number of target offset bits such that each way stores offsets within a particular range. Doing so enables a dramatic reduction in storage for target addresses. Our final design, called BTB-X, uses an 8-way set associative BTB with differently sized ways that enables it to track about 2.24x more branches than a conventional BTB and 1.3x more branches than a storage-optimized state-of-the-art BTB organization, called PDede, with the same storage budget.

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