CoreTSAR: Core Task-Size Adapting Runtime

CoreTSAR: Task Scheduling for Accelerator-aware Runtimes

Heterogeneous supercomputers that incorporate computational accelerators such as GPUs are increasingly popular due to their high peak performance, energy efficiency and comparatively low cost. Unfortunately, the programming models and frameworks designed to extract performance from all computational units still lack the flexibility of their CPU-only counterparts. Accelerated OpenMP improves thi...

CoreTSAR: Adaptive Worksharing for Heterogeneous Systems

The popularity of heterogeneous computing continues to increase rapidly due to the high peak performance, favorable energy e ciency, and comparatively low cost of accelerators. However, heterogeneous programming models still lack the flexibility of their CPU-only counterparts. Accelerated OpenMP models, including OpenMP 4.0 and OpenACC, ease the migration of code from CPUs to GPUs but lack much...

An Approach of Chunk-based Task Runtime Prediction for Self-Scheduling on Multi-core Desk Grid

Self-Scheduling is a dynamic and adaptive loop scheduling approach to reduce the total execution time for a task running in the cluster or grid environment. This paper focuses on how to use and optimize Self-scheduling technologies to allocate tasks reasonable and achieve better parallel performance. It introduces the prediction algorithms and proposes a novel Chunk-based Task Runtime Predictio...

Modeling and Simulation of a Dynamic Task-Based Runtime System for Heterogeneous Multi-core Architectures

The Nexus Task-parallel Runtime System the Nexus Task-parallel Runtime System

A runtime system provides a parallel language compiler with an interface to the low-level facilities required to support interaction between concurrently executing program components. Nexus is a portable runtime system for task-parallel programming languages. Distinguishing features of Nexus include its support for multiple threads of control, dynamic processor acquisition, dynamic address spac...