Exploring Task Mappings on Heterogeneous MPSoCs using a Bias-Elitist Genetic Algorithm

Exploration of task mappings plays a crucial role in achieving high performance in heterogeneous multi-processor system-on-chip (MPSoC) platforms. The problem of optimally mapping a set of tasks onto a set of given heterogeneous processors for maximal throughput has been known, in general, to be NP-complete. The problem is further exacerbated when multiple applications (i.e., bigger task sets) and the communication between tasks are also considered. Previous research has shown that Genetic Algorithms (GA) typically are a good choice to solve this problem when the solution space is relatively small. However, when the size of the problem space increases, classic genetic algorithms still suffer from the problem of long evolution times. To address this problem, this paper proposes a novel bias-elitist genetic algorithm that is guided by domain-specific heuristics to speed up the evolution process. Experimental results reveal that our proposed algorithm is able to handle large scale task mapping problems and produces high-quality mapping solutions in only a short time period.Comment: 9 pages, 11 figures, uses algorithm2e.st

A Run-time Self-adaptive Resource Allocation Framework for MPSoC Systems

Self-adaptivity is becoming a key feature of modern embedded systems to meet performance and power constraints in increasingly common situations where embedded application workloads show highly dynamic behavior. This paper presents a scalable framework for adaptive MultiProcessor System-on-Chip (MPSoC) systems that allows for adaptivity throttling

Performance Estimation of Task Graphs Based on Path Profiling

Correctly estimating the speed-up of a parallel embedded application is crucial to efficiently compare different parallelization techniques, task graph transformations or mapping and scheduling solutions. Unfortunately, especially in case of control-dominated applications, task correlations may heavily affect the execution time of the solutions and usually this is not properly taken into account during performance analysis. We propose a methodology that combines a single profiling of the initial sequential specification with different decisions in terms of partitioning, mapping, and scheduling in order to better estimate the actual speed-up of these solutions. We validated our approach on a multi-processor simulation platform: experimental results show that our methodology, effectively identifying the correlations among tasks, significantly outperforms existing approaches for speed-up estimation. Indeed, we obtained an absolute error less than 5 % in average, even when compiling the code with different optimization levels