Parallelization of irregular algorithms for shape detection

Processor Allocation for Optimistic Parallelization of Irregular Programs

Optimistic parallelization is a promising approach for the parallelization of irregular algorithms: potentially interfering tasks are launched dynamically, and the runtime system detects conflicts between concurrent activities, aborting and rolling back conflicting tasks. However, parallelism in irregular algorithms is very complex. In a regular algorithm like dense matrix multiplication, the a...

On the parallelization of irregular and dynamic programs

Current compilers show ineffective when optimizing complex applications, both analyzing dependences and exploiting data locality and extracting parallelism. Complex applications may be characterized as irregular and dynamic. Irregular applications arrange data as multidimensional arrays and memory is referenced through array indirections. Dynamic applications organize data as pointer-based stru...

Simulated Annealing and Genetic Algorithms for Shape Detection

The paper presents three heuristical methods for the solution of the shape detection problem. This problem arises in a large number of applications and is therefore of large interest to develop eeective methods for its solution. We formulate the shape detection problem as a combinatorial optimization problem and use methods based on simulated annealing and genetic algorithms for its solution. T...

Automatic Parallelization of Irregular and Pointer-Based Computations: Perspectives from Logic and Constraint Programming

Abs t r ac t . Irregular computations pose some of the most interesting and challenging problems in automatic parallelization. Irregularity appears in certain kinds of numerical problems and is pervasive in symbolic applications. Such computations often use dynamic data structures which make heavy use of pointers. This complicates all the steps of a parallelizing compiler, from independence det...

Multi-gpu Parallelization of Irregular Algorithms