Graphics processing units (GPUs) provide both memory bandwidth and arithmetic performance far greater than that available on CPUs but, because of their Single-Instruction-Multiple...
This article presents a general algorithm for transforming sequential imperative programs into parallel data-flow programs. Our algorithm operates on a program dependence graph i...
Software-based thread-level parallelization has been widely studied for exploiting data parallelism in purely computational loops to improve program performance on multiprocessors...
The actor model has already proven itself as an interesting concurrency model that avoids issues such as deadlocks and race conditions by construction, and thus facilitates concur...
In this demonstration we present BRRL, a library for making distributed main-memory applications fault tolerant. BRRL is optimized for cloud applications with frequent points of c...
Tuan Cao, Benjamin Sowell, Marcos Antonio Vaz Sall...
A new challenge in scientific computing is to merge existing simulation models to create new higher fidelity combined (often multi-level) models. While this challenge has been a...
For more than thirty years, the parallel programming community has used the dependence graph as the main abstraction for reasoning about and exploiting parallelism in “regular...
Keshav Pingali, Donald Nguyen, Milind Kulkarni, Ma...
MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typ...
Scripting languages such as R and Matlab are widely used in scientific data processing. As the data volume and the complexity of analysis tasks both grow, sequential data process...
Jiangtian Li, Xiaosong Ma, Srikanth B. Yoginath, G...
Many biologically motivated problems are expressed as dynamic programming recurrences and are difficult to parallelize due to the intrinsic data dependencies in their algorithms. ...
Narayan Ganesan, Roger D. Chamberlain, Jeremy Buhl...