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Evan Speight

Researcher at IBM

Publications -  27
Citations -  1384

Evan Speight is an academic researcher from IBM. The author has contributed to research in topics: Cache & Distributed shared memory. The author has an hindex of 15, co-authored 27 publications receiving 1340 citations. Previous affiliations of Evan Speight include Cornell University & Rice University.

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Proceedings ArticleDOI

Hybrid cache architecture with disparate memory technologies

TL;DR: This paper discusses and evaluates two types of hybrid cache architectures: inter cache Level HCA (LHCA), in which the levels in a cache hierarchy can be made of disparate memory technologies; and intra cache level or cache Region based H CA (RHCA), where a single level of cache can be partitioned into multiple regions, each of a different memory technology.
Journal ArticleDOI

Mambo: a full system simulator for the PowerPC architecture

TL;DR: The experience in implementing the simulator and its uses within IBM to model future systems, support early software development, and design new system software are described.
Proceedings Article

Brazos: a third generation DSM system

TL;DR: The Brazos design and implementation is described, and its performance running five scientific applications to the performance of Solaris and Windows NT implementations of the TreadMarks DSM system running on the same hardware is compared.
Proceedings ArticleDOI

Power and performance of read-write aware hybrid caches with non-volatile memories

TL;DR: It is demonstrated that a RWHCA design with a conservative setup can provide a geometric mean 55% power reduction and yet 5% IPC improvement over a baseline SRAM cache design across a collection of 30 workloads.
Journal ArticleDOI

Adaptive Mechanisms and Policies for Managing Cache Hierarchies in Chip Multiprocessors

TL;DR: This paper proposes simple architectural extensions and adaptive policies for managing the L2 and L3 cache hierarchy in a CMP system and evaluates two mechanisms that improve cache effectiveness, observing a reduction in the overall execution time of up to 13%.