Demo Data from EDBT 2026 / reviewers in the wild / expert
Sangam Jindal
dblp:223/7841
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Processor architecture and microarchitecture › branch prediction
branch target buffer |
0.3 | 1 | 2018 | Exploring Predictive Replacement Policies for Instruction Cache and Branch Target Buffer · ISCA 2018 |
Memory systems › cache management
cache replacement |
0.3 | 1 | 2018 | Exploring Predictive Replacement Policies for Instruction Cache and Branch Target Buffer · ISCA 2018 |
Memory systems › cache › CPU cache
instruction cache |
0.1 | 1 | 2018 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Exploring Predictive Replacement Policies for Instruction Cache and Branch Target BufferabstractModern 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 |
ISCA | 3 |