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Ben Serebrin

dblp:29/6580 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2008
—ORCID · none

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

Systems, architecture and hardware · 3 · 1 first-authorSoftware 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 · 46% Cloud and datacenter computing · 46% Processor architecture and microarchitecture · 7%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing › virtualization
nested paging
0.112008
Accelerating two-dimensional page walks for virtualized systems · ASPLOS 2008
Memory systems › memory management › virtual memory
page table walk
0.112008
Accelerating two-dimensional page walks for virtualized systems · ASPLOS 2008
Cloud and datacenter computing
virtualization
0.112008
Accelerating two-dimensional page walks for virtualized systems · ASPLOS 2008
Memory systems › memory management
virtual memory
0.112008
Accelerating two-dimensional page walks for virtualized systems · ASPLOS 2008

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

nested paging · 0.1
YearPublicationVenuePosition
2008 Accelerating two-dimensional page walks for virtualized systems
abstract
Nested paging is a hardware solution for alleviating the software memory management overhead imposed by system virtualization. Nested paging complements existing page walk hardware to form a two-dimensional (2D) page walk, which reduces the need for hypervisor intervention in guest page table management. However, the extra dimension also increases the maximum number of architecturally-required page table references.
Ravi Bhargava, Ben Serebrin, Francesco Spadini, Srilatha Manne
ASPLOS2
2002 Media Processing Applications on the Imagine Stream Processor
abstract
Media applications, such as image processing, signal processing, video, and graphics, require high computation rates and data bandwidths. The stream programming model is a natural and powerful way to describe these applications. Expressing media applications in this model allows hardware and software systems to take advantage of their concurrency and locality in order to meet their high computational demands. The Imagine stream programming system, a set of software tools and algorithms, is used to program media applications in the stream programming model. We achieve real-time performance on a variety of media processing applications with high computation rates (4-15 billion achieved operations per second) and high efficiency (84-95% occupancy on the arithmetic clusters).
John D. Owens, Scott Rixner, Ujval J. Kapasi, Peter R. Mattson, Brian Towles, Ben Serebrin, William J. Dally
ICCD6
2002 A Stream Processor Development Platform
abstract
We describe a hardware and software platform for developing streaming applications. Programmers write stream programs in high-level languages, and a set of software tools maps these programs to code that runs on a streaming hardware system. The hardware platform includes two Imagine stream processors, together providing 32 GFLOPS peak performance, and a high-speed onboard network to carry video and other data between peripherals and the Imagine processors.
Ben Serebrin, John D. Owens, Chen H. Chen, Stephen P. Crago, Ujval J. Kapasi, Peter R. Mattson, Jinyung Namkoong, Scott Rixner, William J. Dally
ICCD1