Exploring predictive replacement policies for instruction cache and branch target buffer

Modern processors support instruction fetch with the instruction cache (I-cache) and branch target buffer (BTB). Due to timing and area constraints, the I-cache and BTB must efficiently make use of their limited capacities. Blocks in the I-cache or entries in the BTB that have low potential for reus...

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Bibliographic Details
Main Authors: Ajorpaz, Samira Mirbagher, Garza, Elba, Jindal, Sangam, Jiménez, Daniel A.
Format: Conference Proceeding
Language:English
Subjects:
branch prediction
branch target buffer
General and reference
General and reference > Cross-computing tools and techniques
General and reference > Cross-computing tools and techniques > Performance
History
Indexes
instruction fetch
Metadata
microarchitecture
Optimization
Prefetching
Timing
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Summary:Modern processors support instruction fetch with the instruction cache (I-cache) and branch target buffer (BTB). Due to timing and area constraints, the I-cache and BTB must efficiently make use of their limited capacities. Blocks in the I-cache or entries in the BTB that have low potential for reuse should be replaced by more useful blocks/entries. This work explores predictive replacement policies based on reuse prediction that can be applied to both the I-cache and BTB. Using a large suite of recently released industrial traces, we show that predictive replacement policies can reduce misses in the I-cache and BTB. We introduce Global History Reuse Prediction (GHRP), a replacement technique that uses the history of past instruction addresses and their reuse behaviors to predict dead blocks in the I-cache and dead entries in the BTB. This paper describes the effectiveness of GHRP as a dead block replacement and bypass optimization for both the I-cache and BTB. For a 64KB set-associative I-cache with a 64B block size, GHRP lowers the I-cache misses per 1000 instructions (MPKI) by an average of 18% over the least-recently-used (LRU) policy on a set of 662 industrial workloads, performing significantly better than Static Re-reference Interval Prediction (SRRIP) [1] and Sampling Dead Block Prediction (SDBP)[2]. For a 4K-entry BTB, GHRP lowers MPKI by an average of 30% over LRU, 23% over SRRIP, and 29% over SDBP.
ISSN:2575-713X
DOI:10.1109/ISCA.2018.00050