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Krzysztof Rutkowski

dblp:14/2264 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 1Applied, interdisciplinary, general and emerging computing · 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
Parallel and multicore computing · 56% Processor architecture and microarchitecture · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Processor architecture and microarchitecture
chip multiprocessor
0.112007
Accelerating Sequential Applications on CMPs Using Core Spilling · IEEE Trans. Parallel Distributed Syst. 2007
Parallel and multicore computing › parallel programming runtimes › thread management
thread migration
0.112007
Accelerating Sequential Applications on CMPs Using Core Spilling · IEEE Trans. Parallel Distributed Syst. 2007
Parallel and multicore computing › task scheduling › process scheduling
multitask scheduling
0.012007
Accelerating Sequential Applications on CMPs Using Core Spilling · IEEE Trans. Parallel Distributed Syst. 2007

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

simulation · 0.1
YearPublicationVenuePosition
2011 Improving the predictiveness of ICU medical scales by the method of pairwise comparisons
Mohammed A. Alqarni, Yassen Arabi, Tamar Kakiashvili, Mohammed Khedr, Waldemar W. Koczkodaj, J. Leszek, Artur Przelaskowski, Krzysztof Rutkowski
FedCSIS8
2007 Accelerating Sequential Applications on CMPs Using Core Spilling
abstract
Chip multiprocessors (CMPs) provide a scalable means of exploiting thread-level parallelism for multitasking or multithreaded applications. However, single-threaded applications will have difficulty dynamically leveraging the statically partitioned resources in a CMP. Such sequential applications may be difficult to statically decompose into threads or may simply be-a legacy code where recompilation is not possible or cost-effective. We present a novel approach to dynamically accelerate the performance of sequential application(s) on multiple cores. Execution is allowed to spill from one core to another when resources on one core have been exhausted. We propose two techniques to enable low-overhead migration between cores: prespilling and locality-based filtering. We develop and analyze an arbitration mechanism to intelligently allocate cores among a set of sequential applications on a CMP. On average, core spilling on an eight-core CMP can accelerate single-threaded performance by 35 percent. We further explore an eight-core CMP running a multiple application workload composed of the entire SPEC 2000 benchmark suite in various combinations and arrival times. Using core spilling to accelerate the current set of running applications in cases where there are idle cores, we achieve up to a 40 percent improvement in performance.
Jason Cong, Guoling Han, Ashok Jagannathan, Glenn Reinman, Krzysztof Rutkowski
IEEE Trans. Parallel Distributed Syst.5