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Sangam Jindal

dblp:223/7841 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Memory systems · 56% Processor architecture and microarchitecture · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Processor architecture and microarchitecture › branch prediction
branch target buffer
0.312018
Exploring Predictive Replacement Policies for Instruction Cache and Branch Target Buffer · ISCA 2018
Memory systems › cache management
cache replacement
0.312018
Exploring Predictive Replacement Policies for Instruction Cache and Branch Target Buffer · ISCA 2018
Memory systems › cache › CPU cache
instruction cache
0.112018
Exploring Predictive Replacement Policies for Instruction Cache and Branch Target Buffer · ISCA 2018

Methods — techniques the papers use, named apart from their topics

reuse prediction · 0.3dead block prediction · 0.3
YearPublicationVenuePosition
2018 Exploring Predictive Replacement Policies for Instruction Cache and Branch Target Buffer
abstract
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) and Sampling Dead Block Prediction (SDBP). For a 4K-entry BTB, GHRP lowers MPKI by an average of 30% over LRU, 23% over SRRIP, and 29% over SDBP.
Samira Mirbagher Ajorpaz, Elba Garza, Sangam Jindal, Daniel A. Jiménez
ISCA3