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Cornell University

High Performance Sequential Execution In Fine-Grain Multicore Processors Via Core Aggregation

Abstract

dc:description.abstract

This dissertation presents core fusion, a reconfigurable chip multiprocessor (CMP) architecture where groups of fundamentally independent cores can dynamically morph into a larger CPU, or they can be used as distinct processing elements, as needed at run time by applications. Core fusion improves sequentialcode performance and thus gracefully accommodates software diversity in future’s highly-parallel CMPs. It provides a single execution model across all configurations, requires no additional programming effort or specialized compiler support, maintains ISA compatibility, and leverages mature micro-architecture technology. We first present an effective approach to dynamically fuse multiple narrowissue out-of-order cores into a more powerful out-of-order execution engine. The use of out-of-order base cores provides the design with valuable opportunities for latency hiding. Next, we present a second set of mechanisms to dynamically fuse multiple in-order cores into a more powerful out-of-order execution engine. In-order cores are extremely power-efficient and simple, and they help maximize core count, which is ideal for exploiting thread-level parallelism (TLP). However, sequential-code performance is significantly degraded. Enabling core fusion on such substrates proves to be very effective in boosting performance, and only with relatively small hardware overhead.

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Kirman, Meyrem

Rights

Language dc:language.iso
en_US

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1813/14888
OAI identifier oai:identifier
oai:ecommons.cornell.edu:1813/14888

Chain of custody

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Harvested from
Cornell University
Base URL
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Last updated
2026-07-24
Source record
OAI-PMH GetRecord
related terms
citation

Kirman, Meyrem. High Performance Sequential Execution In Fine-Grain Multicore Processors Via Core Aggregation. 2010. https://hdl.handle.net/1813/14888