Abstract
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.
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.
Original language | English |
---|---|
Pages | 125-138 |
Number of pages | 14 |
Publication status | Accepted/In press - 16 Apr 2021 |
Event | The 17th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE’21) - Virtual Duration: 16 Apr 2021 → 16 Apr 2021 Conference number: 17 https://conf.researchr.org/home/vee-2021 |
Conference
Conference | The 17th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE’21) |
---|---|
Abbreviated title | VEE |
Period | 16/04/21 → 16/04/21 |
Internet address |
Keywords
- JVM
- Heterogeneous Hardware
- Multi-threading
- Hardware
- Bytecodes