Degree Granting Department
Computer Science and Engineering
Nagarajan Ranganathan Ph.D.
Srinivas Katkoori, Ph.D.
Hao Zheng, Ph.D.
Tapas Das, Ph.D.
Manish Agrawal, Ph.D.
Code Partitioning, Heterogeneous Systems, Distributed Execution, Code Generation
In distributed heterogeneous systems the partitioning of application software to be executed in a distributed fashion is a challenge by itself. The task of code partitioning for distributed processing involves partitioning the code into clusters and mapping those code clusters to the individual processing elements interconnected through a high speed network. Code generation is the process of converting the code partitions into individually executable code clusters and satisfying the code dependencies by adding communication primitives to send and receive data between dependent code clusters. In this work, we describe a generalized framework for automatic code partitioning and code generation for distributed heterogeneous systems. A model for system level design and synthesis using transaction level models has also been developed and is presented. The application programs along with the partition primitives are converted into independently executable concrete implementations. The process consists of two steps, first translating the primitives of the application program into equivalent code clusters, and then scheduling the implementations of these code clusters according to the inherent data dependencies. Further, the original source code needs to be reverse engineered in order to create a meta-data table describing the program elements and dependency trees. The data gathered, is used along with Parallel Virtual Machine (PVM) primitives for enabling the communication between the partitioned programs in the distributed environment. The framework consists of profiling tools, partitioning methodology, architectural exploration and cost analysis tools. The partitioning algorithm is based on clustering, in which the code clusters are created to minimize communication overhead represented as data transfers in task graph for the code. The proposed approach has been implemented and tested for different applications and compared with simulated annealing and tabu search based partitioning algorithms. The objective of partitioning is to minimize the communication overhead. While the proposed approach performs comparably with simulated annealing and better than tabu search based approaches in most cases in terms of communication overhead reduction, it is conclusively faster than simulated annealing and tabu search by an order of magnitude as indicated by simulation results. The proposed framework for system level design/synthesis provides an end to end rapid prototyping approach for aiding in architectural exploration and design optimization. The level of abstraction in the design phase can be fine tuned using transaction level models.
Scholar Commons Citation
Sairaman, Viswanath, "A Generalized Framework for Automatic Code Partitioning and Generation in Distributed Systems" (2010). USF Tampa Graduate Theses and Dissertations.