Fakultät für Informatik
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Technische Universität München
Lehrstuhl für Effiziente Algorithmen
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Parallelizing heavyweight debugging tools with mpiecho
Barry Rountree
,
Todd Gamblin
,
Bronis R. de Supinski
,
Martin Schulz
,
David K. Lowenthal
,
Guy Cobb
,
Henry Tufo
Parallel Computing
39
(3), 2013, pp. 156-166
ScalaTrace: Scalable compression and replay of communication traces for high-performance computing
Michael Noeth
,
Prasun Ratn
,
Frank Mueller
,
Martin Schulz
,
Bronis R. de Supinski
Journal Parallel Distributed Computing
69
(8), 2009, pp. 696-710
A novel approach for data distribution on NUMA machines
Jie Tai
,
Wolfgang Karl
,
Martin Schulz
Mitteilungen - Gesellschaft für Informatik e.V., Parallel-Algorithmen und Rechnerstrukturen
(19), 2002, pp. 27-33
A novel approach for data distribution on NUMA machines
Jie Tao
,
Wolfgang Karl
,
Martin Schulz
Proceedings of the Workshops on Dependability and Fault Tolerance -Java in Embedded Systems - Parallel Systems and Algorithms PASA - in Conjunction with the International Conference on Architecture of Computing Systems - Trends in Network and Pervasive Computing, ARCS'2002 (Karlsruhe, Germany, April 8-12, 2002)
, 2002, pp. 189-195
Evaluating a novel hybrid-DSM framework in real-world scenarios using medical imaging applications
Martin Schulz
Mitteilungen - Gesellschaft für Informatik e.V., Parallel-Algorithmen und Rechnerstrukturen
(18), 2001, pp. 40-49
A framework for monitoring shared memory applications
Jie Tao
,
Martin Schulz
Mitteilungen - Gesellschaft für Informatik e.V., Parallel-Algorithmen und Rechnerstrukturen
(18), 2001, pp. 50-59
NEPHEW: Applying a toolset for the efficient deployment of a medical image application on SCI-based clusters
Wolfgang Karl
,
Martin Schulz
,
Martin Völk
,
Sibylle Ziegler
Lecture Notes in Computer Science
1900
, 2000, pp. 851-860
SCI-VM: A flexible base for transparent shared memory programming models on clusters of PCs
Martin Schulz
Lecture Notes in Computer Science
1586
, 1999, pp. 19-33
True shared memory programming on SCI-based clusters
Martin Schulz
Lecture Notes in Computer Science
1734
, 1999, pp. 291-311