We present Celias, a new concurrent programming model for data-intensive scalable computing. Celias supports many virtues commonly found in existing distributed pro-gramming frameworks, such as elastic scaling and fault tolerance, without sacrificing expressiveness. The key de-sign idea of Celias is the concept of a microtask, as a scalable, fault-tolerant, and completely data-driven unit of computation. By combining Tuplespace and microtasks, Celias provides an intuitive yet powerful programming abstraction for large and complex problems.
Many-core architectures are a commercial reality, but programming them efficiently is still a challe...
© 2017 IEEE. The overwhelming wealth of parallelism exposed by Extreme-scale computing is rekindling...
This paper presents two complementary statistical computing frameworks that address challenges in pa...
Symbolic computation is an important area of both Mathematics and Computer Science, with many large...
International audienceIn this paper, we focus on a distributed and parallel programming paradigm for...
International audienceExtreme scale parallel computing systems will have tens of thousands of option...
any of the information contained in it must acknowledge this thesis as the source of the quotation o...
Symbolic computation is an important area of both Mathematics and Computer Science, with many large ...
Generalizable approaches, models, and frameworks for irregular application scalability is an old yet...
Abstract. In this paper we introduce the Concurrent Collections pro-gramming model, which builds on ...
grantor: University of TorontoWe introduce the novel 'scoped behaviour' patterns within th...
Common many-core processors contain tens of cores and distributed memory. Compared to a multicore sy...
Exploiting parallelism in modern machines increases the di culty of developing applications. Thus, ...
We propose a new approach to programming multi-core, relaxed-memory architectures in imperative, por...
The next generations of supercomputers are projected to have hun-dreds of thousands of processors. H...
Many-core architectures are a commercial reality, but programming them efficiently is still a challe...
© 2017 IEEE. The overwhelming wealth of parallelism exposed by Extreme-scale computing is rekindling...
This paper presents two complementary statistical computing frameworks that address challenges in pa...
Symbolic computation is an important area of both Mathematics and Computer Science, with many large...
International audienceIn this paper, we focus on a distributed and parallel programming paradigm for...
International audienceExtreme scale parallel computing systems will have tens of thousands of option...
any of the information contained in it must acknowledge this thesis as the source of the quotation o...
Symbolic computation is an important area of both Mathematics and Computer Science, with many large ...
Generalizable approaches, models, and frameworks for irregular application scalability is an old yet...
Abstract. In this paper we introduce the Concurrent Collections pro-gramming model, which builds on ...
grantor: University of TorontoWe introduce the novel 'scoped behaviour' patterns within th...
Common many-core processors contain tens of cores and distributed memory. Compared to a multicore sy...
Exploiting parallelism in modern machines increases the di culty of developing applications. Thus, ...
We propose a new approach to programming multi-core, relaxed-memory architectures in imperative, por...
The next generations of supercomputers are projected to have hun-dreds of thousands of processors. H...
Many-core architectures are a commercial reality, but programming them efficiently is still a challe...
© 2017 IEEE. The overwhelming wealth of parallelism exposed by Extreme-scale computing is rekindling...
This paper presents two complementary statistical computing frameworks that address challenges in pa...