PDede: Partitioned, Deduplicated, Delta Branch Target Buffer

Soundararajan, Niranjan; Braun, Peter; Khan, Tanvir Ahmed; Kasikci, Baris; Litz, Heiner; Subramoney, Sreenivas

doi:10.1145/3466752.3480046

Cited by 13 publications

(19 citation statements)

References 52 publications

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“…Shotgun [83] faces a slight slowdown as it wastes critical BTB capacity to store unused prefetch metadata. We corroborate the findings [23,75,132] of recent work and identify three reasons behind the performance degradation induced by these prior BTB prefetching policies [73,83]. First, like any temporal prefetcher [139][140][141],…”

Section: Why Is the Btb Replacement Policy Important?supporting

confidence: 88%

“…As also observed by recent work [75,132], we find in Fig. 4 that Confluence [73] [73,83] provide merely 1.4% mean speedup, while a perfect BTB offers 63.2% mean speedup.…”

Section: Why Is the Btb Replacement Policy Important?supporting

confidence: 87%

“…These results indicate that with a perfect BTB, FDIP can provide more benefits than with a perfect I-cache or a perfect branch predictor. Hence, optimizing BTB performance is critical to eliminate frontend stalls in the most efficient manner (as also reported by prior work [75,83,84,132]).…”

Section: Why Is the Btb Replacement Policy Important?mentioning

confidence: 77%

“…Prior works have found that FDIP's performance is significantly limited by BTB misses that stall FDIP's prefetching [23,75,83,84] or cause FDIP to prefetch incorrect instructions on the wrong path [44,132]. As we and others [23,75,83,84,132] show, this limitation inhibits FDIP from eliminating more than 40% of all frontend stalls in data center applications.…”

Section: Introductionmentioning

confidence: 76%

“…However, FDIP performs well only as long as the Branch Target Buffer (BTB) supplies correct targets for all taken branches [23,44,75,83,84,132]. Prior works have found that FDIP's performance is significantly limited by BTB misses that stall FDIP's prefetching [23,75,83,84] or cause FDIP to prefetch incorrect instructions on the wrong path [44,132].…”

Section: Introductionmentioning

confidence: 99%

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Thermometer

Song

Khan

Shahri

et al. 2022

Proceedings of the 49th Annual International Symposium on Computer Architecture

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Modern processors employ a decoupled frontend with Fetch Directed Instruction Prefetching (FDIP) to avoid frontend stalls in data center applications. However, the large branch footprint of data center applications precipitates frequent Branch Target Buffer (BTB) misses that prohibit FDIP from eliminating more than 40% of all frontend stalls. We find that the state-of-the-art BTB optimization techniques (e.g., BTB prefetching and replacement mechanisms) cannot eliminate these misses due to their inadequate understanding of branch reuse behavior in data center applications.In this paper, we first perform a comprehensive characterization of the branch behavior of data center applications, and determine that identifying optimal BTB replacement decisions requires considering both transient and holistic (i.e., across the entire execution) branch behavior. We then present Thermometer, a novel BTB replacement technique that realizes the holistic branch behavior via a profile-guided analysis. Based on the collected profile, Thermometer generates useful BTB replacement hints that the underlying hardware can leverage. We evaluate Thermometer using 13 widelyused data center applications and demonstrate that it provides an average speedup of 8.7% (0.4%-64.9%) while outperforming the state-of-the-art BTB replacement techniques by 5.6× (on average, the best performing prior work achieves 1.5% speedup). We also demonstrate that Thermometer achieves a performance speedup that is, on average, 83.6% of the speedup achieved by the optimal BTB replacement policy.

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Section: Why Is the Btb Replacement Policy Important?supporting

confidence: 88%

“…As also observed by recent work [75,132], we find in Fig. 4 that Confluence [73] [73,83] provide merely 1.4% mean speedup, while a perfect BTB offers 63.2% mean speedup.…”

Section: Why Is the Btb Replacement Policy Important?supporting

confidence: 87%

Section: Why Is the Btb Replacement Policy Important?mentioning

confidence: 77%

Section: Introductionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

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Thermometer

Song

Khan

Shahri

et al. 2022

Proceedings of the 49th Annual International Symposium on Computer Architecture

Self Cite

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JACO: JAva Code Layout Optimizer Enabling Continuous Optimization without Pausing Application Services

Lin,

Qin,

Chen

et al. 2023

2023 IEEE International Conference on Cluster Computing (CLUSTER)

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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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PDede: Partitioned, Deduplicated, Delta Branch Target Buffer

Cited by 13 publications

References 52 publications

Thermometer

Thermometer

JACO: JAva Code Layout Optimizer Enabling Continuous Optimization without Pausing Application Services

A Storage-Effective BTB Organization for Servers

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