Harald Prokop

dblp:61/6777 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none

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

Theory of computation · 2Systems, architecture and hardware · 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
2 papers
Memory systems · 89% Performance modeling and evaluation · 11%
Theoretical computer science
2 papers
Algorithms and data structures · 54% Computational complexity · 46%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems › memory hierarchy
cache hierarchy
0.222012
Cache-Oblivious Algorithms · ACM Trans. Algorithms 2012
Cache-Oblivious Algorithms · FOCS 1999
Memory systems › cache
cache-oblivious algorithms
0.222012
Cache-Oblivious Algorithms · ACM Trans. Algorithms 2012
Cache-Oblivious Algorithms · FOCS 1999
Computational complexity › algebraic complexity
matrix multiplication
0.112012
Cache-Oblivious Algorithms · ACM Trans. Algorithms 2012
Algorithms and data structures › sequence algorithms
sorting
0.112012
Cache-Oblivious Algorithms · ACM Trans. Algorithms 2012
Performance modeling and evaluation
cache model
0.012012
Cache-Oblivious Algorithms · ACM Trans. Algorithms 2012
Memory systems
memory hierarchy
0.011999
Cache-Oblivious Algorithms · FOCS 1999
Algorithms and data structures › memory hierarchy
external memory algorithms
0.011999
Cache-Oblivious Algorithms · FOCS 1999

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

ideal-cache model · 0.3LRU replacement · 0.3empirical evaluation · 0.0asymptotic analysis · 0.0
YearPublicationVenuePosition
2012 Cache-Oblivious Algorithms
abstract
This article presents asymptotically optimal algorithms for rectangular matrix transpose, fast Fourier transform (FFT), and sorting on computers with multiple levels of caching. Unlike previous optimal algorithms, these algorithms are cache oblivious : no variables dependent on hardware parameters, such as cache size and cache-line length, need to be tuned to achieve optimality. Nevertheless, these algorithms use an optimal amount of work and move data optimally among multiple levels of cache. For a cache with size M and cache-line length B where M = Ω ( B 2 ), the number of cache misses for an m × n matrix transpose is Θ (1 + mn / B ). The number of cache misses for either an n -point FFT or the sorting of n numbers is Θ (1 + ( n / B )(1 + log M n )). We also give a Θ ( mnp )-work algorithm to multiply an m × n matrix by an n × p matrix that incurs Θ (1 + ( mn + np + mp )/ B + mnp / B √ M ) cache faults. We introduce an “ideal-cache” model to analyze our algorithms. We prove that an optimal cache-oblivious algorithm designed for two levels of memory is also optimal for multiple levels and that the assumption of optimal replacement in the ideal-cache model can be simulated efficiently by LRU replacement. We offer empirical evidence that cache-oblivious algorithms perform well in practice.
Matteo Frigo, Charles E. Leiserson, Harald Prokop, Sridhar Ramachandran
ACM Trans. Algorithms3
2009 Guest Editors' Introduction: Special Section on Autonomic Network Computing
abstract
The seven papers in this special section focus on autonomic network computing.
Dimiter R. Avresky, Harald Prokop, Dinesh C. Verma
IEEE Trans. Computers2
1999 Cache-Oblivious Algorithms
abstract
This paper presents asymptotically optimal algorithms for rectangular matrix transpose, FFT, and sorting on computers with multiple levels of caching. Unlike previous optimal algorithms, these algorithms are cache oblivious: no variables dependent on hardware parameters, such as cache size and cache-line length, need to be tuned to achieve optimality. Nevertheless, these algorithms use an optimal amount of work and move data optimally among multiple levels of cache. For a cache with size Z and cache-line length L where Z=/spl Omega/(L/sup 2/) the number of cache misses for an m/spl times/n matrix transpose is /spl Theta/(1+mn/L). The number of cache misses for either an n-point FFT or the sorting of n numbers is /spl Theta/(1+(n/L)(1+log/sub Z/n)). We also give an /spl Theta/(mnp)-work algorithm to multiply an m/spl times/n matrix by an n/spl times/p matrix that incurs /spl Theta/(1+(mn+np+mp)/L+mnp/L/spl radic/Z) cache faults. We introduce an "ideal-cache" model to analyze our algorithms. We prove that an optimal cache-oblivious algorithm designed for two levels of memory is also optimal for multiple levels and that the assumption of optimal replacement in the ideal-cache model. Can be simulated efficiently by LRU replacement. We also provide preliminary empirical results on the effectiveness of cache-oblivious algorithms in practice.
Matteo Frigo, Charles E. Leiserson, Harald Prokop, Sridhar Ramachandran
FOCS3