Multiple-Tasks on Multiple-Devices (MTMD): Exploiting Concurrency in Heterogeneous Managed Runtimes

Research output: Contribution to conferencePaperpeer-review

  • External authors:
  • Michail Papadimitriou
  • Eleni Markou
  • Florin-Gabriel Blanaru


Modern commodity devices are nowadays equipped with a plethora of heterogeneous devices serving different purposes. Being able to exploit such heterogeneous hardware accelerators to their full potential is of paramount importance in the pursuit of higher performance and energy efficiency. Towards these objectives, the reduction of idle time of each device as well as the concurrent program execution across different accelerators can lead to better scalability within the computing platform.
In this work, we propose a novel approach for enabling a Java-based heterogeneous managed runtime to automatically and efficiently deploy multiple tasks on multiple devices. We extend TornadoVM with parallel execution of bytecode interpreters to dynamically and concurrently manage and execute arbitrary tasks across multiple OpenCL-compatible devices. In addition, in order to achieve an efficient device-task allocation, we employ a machine learning approach with a multiple-classification architecture of Extra-Trees-Classifiers. Our proposed solution has been evaluated over a suite of 12 applications split into three different groups. Our experimental results showcase performance improvements up 83% compared to all tasks running on the single best device, while reaching up to 91% of the oracle performance.

Bibliographical metadata

Original languageEnglish
Number of pages14
Publication statusAccepted/In press - 16 Apr 2021
EventThe 17th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE’21) - Virtual
Event duration: 16 Apr 202116 Apr 2021
Conference number: 17


ConferenceThe 17th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE’21)
Abbreviated titleVEE
Internet address