Non-intrusive on-the-fly data race detection using execution replay

This paper presents a practical solution for detecting data races in parallel programs. The solution consists of a combination of execution replay (RecPlay) with automatic on-the-fly data race detection. This combination enables us to perform the data race detection on an unaltered execution (almost no probe effect). Furthermore, the usage of multilevel bitmaps and snooped matrix clocks limits the amount of memory used. As the record phase of RecPlay is highly efficient, there is no need to switch it off, hereby eliminating the possibility of Heisenbugs because tracing can be left on all the time.Comment: In M. Ducasse (ed), proceedings of the Fourth International Workshop on Automated Debugging (AAdebug 2000), August 2000, Munich. cs.SE/001003

Parallel Unsmoothed Aggregation Algebraic Multigrid Algorithms on GPUs

We design and implement a parallel algebraic multigrid method for isotropic graph Laplacian problems on multicore Graphical Processing Units (GPUs). The proposed AMG method is based on the aggregation framework. The setup phase of the algorithm uses a parallel maximal independent set algorithm in forming aggregates and the resulting coarse level hierarchy is then used in a K-cycle iteration solve phase with a $\ell^1$-Jacobi smoother. Numerical tests of a parallel implementation of the method for graphics processors are presented to demonstrate its effectiveness.Comment: 18 pages, 3 figure

An Experimental Microarchitecture for a Superconducting Quantum Processor

Quantum computers promise to solve certain problems that are intractable for classical computers, such as factoring large numbers and simulating quantum systems. To date, research in quantum computer engineering has focused primarily at opposite ends of the required system stack: devising high-level programming languages and compilers to describe and optimize quantum algorithms, and building reliable low-level quantum hardware. Relatively little attention has been given to using the compiler output to fully control the operations on experimental quantum processors. Bridging this gap, we propose and build a prototype of a flexible control microarchitecture supporting quantum-classical mixed code for a superconducting quantum processor. The microarchitecture is based on three core elements: (i) a codeword-based event control scheme, (ii) queue-based precise event timing control, and (iii) a flexible multilevel instruction decoding mechanism for control. We design a set of quantum microinstructions that allows flexible control of quantum operations with precise timing. We demonstrate the microarchitecture and microinstruction set by performing a standard gate-characterization experiment on a transmon qubit.Comment: 13 pages including reference. 9 figure

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle

Parallel Graph Partitioning for Complex Networks

Processing large complex networks like social networks or web graphs has recently attracted considerable interest. In order to do this in parallel, we need to partition them into pieces of about equal size. Unfortunately, previous parallel graph partitioners originally developed for more regular mesh-like networks do not work well for these networks. This paper addresses this problem by parallelizing and adapting the label propagation technique originally developed for graph clustering. By introducing size constraints, label propagation becomes applicable for both the coarsening and the refinement phase of multilevel graph partitioning. We obtain very high quality by applying a highly parallel evolutionary algorithm to the coarsened graph. The resulting system is both more scalable and achieves higher quality than state-of-the-art systems like ParMetis or PT-Scotch. For large complex networks the performance differences are very big. For example, our algorithm can partition a web graph with 3.3 billion edges in less than sixteen seconds using 512 cores of a high performance cluster while producing a high quality partition -- none of the competing systems can handle this graph on our system.Comment: Review article. Parallelization of our previous approach arXiv:1402.328