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- Jeffrey Michael Napper, Paolo Bientinesi
- UCHPC-MAW '09
- 2009

Computing as a utility has reached the mainstream. Scientists can now rent time on large commercial clusters through several vendors. The cloud computing model provides flexible support for "pay as you go" systems. In addition to no upfront investment in large clusters or supercomputers, such systems incur no maintenance costs. Furthermore, they can be… (More)

- Paolo Bientinesi, John A. Gunnels, Margaret E. Myers, Enrique S. Quintana-Ortí, Robert A. van de Geijn
- ACM Trans. Math. Softw.
- 2005

In this article we present a systematic approach to the derivation of families of high-performance algorithms for a large set of frequently encountered dense linear algebra operations. As part of the derivation a constructive proof of the correctness of the algorithm is generated. The article is structured so that it can be used as a tutorial for novices.… (More)

- Paolo Bientinesi, Enrique S. Quintana-Ortí, Robert A. van de Geijn
- ACM Trans. Math. Softw.
- 2005

In this article, we present a number of Application Program Interfaces (APIs) for coding linear algebra algorithms. On the surface, these APIs for the MATLAB M-script and C programming languages appear to be simple, almost trivial, extensions of those languages. Yet with them, the task of programming and maintaining families of algorithms for a broad… (More)

This paper describes SuperMatrix, a runtime system that parallelizes matrix operations for SMP and/or multi-core architectures. We use this system to demonstrate how code described at a high level of abstraction can achieve high performance on such architectures while completely hiding the parallelism from the library programmer. The key insight entails… (More)

- Paolo Bientinesi, Inderjit S. Dhillon, Robert A. van de Geijn
- SIAM J. Scientific Computing
- 2005

- Paolo Bientinesi, Roman Iakymchuk, Jeff Napper
- Handbook of Cloud Computing
- 2010

- Paolo Bientinesi, Brian C. Gunter, Robert A. van de Geijn
- ACM Trans. Math. Softw.
- 2008

We study the high-performance implementation of the inversion of a Symmetric Positive Definite (SPD) matrix on architectures ranging from sequential processors to Symmetric MultiProcessors to distributed memory parallel computers. This inversion is traditionally accomplished in three “sweeps”: a Cholesky factorization of the SPD matrix, the… (More)

- Diego Fabregat-Traver, Paolo Bientinesi
- VECPAR
- 2012

We present a prototypical linear algebra compiler that automatically exploits domain-specific knowledge to generate highperformance algorithms. The input to the compiler is a target equation together with knowledge of both the structure of the problem and the properties of the operands. The output is a variety of high-performance algorithms, and the… (More)

- Roman Iakymchuk, Jeff Napper, Paolo Bientinesi
- SAC
- 2011

We investigate the effects of shared resources for high-performance computing in a commercial cloud environment where multiple virtual machines share a single hardware node. Although good performance is occasionally obtained, contention degrades the expected performance and introduces significant variance. Using the DGEMM kernel and the HPL benchmark, we… (More)

- Paul Springer, Jeff R. Hammond, Paolo Bientinesi
- TOMS
- 2017

We present Tensor Transpose Compiler (TTC), an open-source parallel compiler for multidimensional tensor transpositions. To generate high-performance C++ code, TTC explores a number of optimizations, including software prefetching, blocking, loop-reordering, and explicit vectorization. To evaluate the performance of multidimensional transpositions across a… (More)