LOCA Series: addressing challenges in core microarchitecture research

Core microarchitecture research is the study of how to implement an Instruction Set Architecture for a general-purpose processor. The field has been studied for decades, but remains crucial due to the evolving demands of modern computing workloads. Growing instruction footprints, the influx of massive data into the processor, the overhead of modern programming languages, and the emphasis on productivity over performance all require innovative approaches. As Moore's Law reaches its end, the onus of improving performance and efficiency falls on microarchitecture research. Additionally, with more and more companies opting to design their own processors, academia is tasked not only with developing new processing technologies but also training the workforce to design these new chips. In this talk, I will motivate the need for continued core microarchitecture research, give some recent examples of topics we study such as instruction fetch, address translation, and cache management, and give some insight into the challenges we face in this kind of work. For example, branch prediction has been a well-studied topic for decades, but recent trends in software design have caused huge growth in instruction footprints, putting pressure on other areas of instruction fetch as well as overwhelming the capacity of modern branch predictors and ultimately leading to performance degradation. We demonstrate a practical solution for this problem. I will go deep into a couple of microarchitecture research projects and describe the impact our work has had on thought in industry and academia over the years, in particular describing the path from my early work on branch prediction to the state of the art today. I will end by discussing our future work in this area.

Authors

Publication Details

Journal
QRU Quaderns de Recerca en Urbanisme
Published
2024-09-19
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LOCA Series: addressing challenges in core microarchitecture research

Daniel A. Jiménez
QRU Quaderns de Recerca en Urbanisme
Parallel Computing and Optimization Techniques
article

LOCA Series: addressing challenges in core microarchitecture research

Daniel A. Jiménez
article en

Abstract

Core microarchitecture research is the study of how to implement an Instruction Set Architecture for a general-purpose processor. The field has been studied for decades, but remains crucial due to the evolving demands of modern computing workloads. Growing instruction footprints, the influx of massive data into the processor, the overhead of modern programming languages, and the emphasis on productivity over performance all require innovative approaches. As Moore's Law reaches its end, the onus of improving performance and efficiency falls on microarchitecture research. Additionally, with more and more companies opting to design their own processors, academia is tasked not only with developing new processing technologies but also training the workforce to design these new chips. In this talk, I will motivate the need for continued core microarchitecture research, give some recent examples of topics we study such as instruction fetch, address translation, and cache management, and give some insight into the challenges we face in this kind of work. For example, branch prediction has been a well-studied topic for decades, but recent trends in software design have caused huge growth in instruction footprints, putting pressure on other areas of instruction fetch as well as overwhelming the capacity of modern branch predictors and ultimately leading to performance degradation. We demonstrate a practical solution for this problem. I will go deep into a couple of microarchitecture research projects and describe the impact our work has had on thought in industry and academia over the years, in particular describing the path from my early work on branch prediction to the state of the art today. I will end by discussing our future work in this area.

QRU Quaderns de Recerca en Urbanisme
Industry, innovation and infrastructure
Openalex Percentile: Top 46%
Parallel Computing and Optimization Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.