Distributed runtime support for task and data management
Distributed runtime support for task and data management
Parallel programming: techniques and applications using networked workstations and parallel computers
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering
Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering
Superlinear Performance in Real-Time Parallel Computation
The Journal of Supercomputing
Concurrency and Computation: Practice & Experience - The High Performance Architectural Challenge: Mass Market versus Proprietary Components?
Parallel Programming in MATLAB
International Journal of High Performance Computing Applications
A high productivity framework for parallel data intensive computing in matlab
A high productivity framework for parallel data intensive computing in matlab
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
Data-intensive document clustering on graphics processing unit (GPU) clusters
Journal of Parallel and Distributed Computing
A domain-specific approach to heterogeneous parallelism
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
Parallel MATLAB Using Standard MPI Implementations
HPCMP-UGC '10 Proceedings of the 2010 DoD High Performance Computing Modernization Program Users Group Conference
Hi-index | 0.00 |
Computer-aided modeling and simulation are a crucial step in developing, integrating, and optimizing unit operations and subsequently the entire processes in the chemical/pharmaceutical industry. This study details two methods of reducing the computational time to solve complex process models, namely, the population balance model which given the source terms can be very computationally intensive. Population balancemodels are also widely used to describe the time evolutions and distributions of many particulate processes, and its efficient and quick simulation would be very beneficial. The first method illustrates utilization of MATLAB's Parallel Computing Toolbox (PCT) and the second method makes use of another toolbox, JACKET, to speed up computations on the CPU and GPU, respectively. Results indicate significant reduction in computational time for the same accuracy using multicore CPUs. Many-core platforms such as GPUs are also promising towards computational time reduction for larger problems despite the limitations of lower clock speed and device memory. This lends credence to the use of highfidelity models (in place of reduced order models) for control and optimization of particulate processes.