Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

Abstract

As an effective scheme often adopted for performance tuning in many-core processors, task migration provides an opportunity for "hot" tasks to be migrated to run on a "cool" core that has a lower temperature. When a task needs to migrate from one processor core to another, the migration can embark on numerous modes defined by the migration paths undertaken and/or the destinations of the migration. Selecting the right migration mode that a task shall follow has always been difficult, and it can be more challenging with the existence of dark cores that can be called back to service (reactivated), which ushers in additional task migration modes. Previous works have demonstrated that dark cores can be placed near the active cores to reduce power density so that the active cores can run at higher voltage/frequency levels for higher performance. However, the existing task migration schemes neither consider the impact of dark cores on each application's performance, nor exploit performance trade-off under different migration modes. Unlike the existing task migration schemes, in this paper, a runtime task migration algorithm that simultaneously takes both migration modes and dark cores into consideration is proposed, and it essentially has two major steps. In the first step, for a specific migration mode that is tied to an application whose tasks need to be migrated, the number of dark cores is determined so that the overall performance is maximized. The second step is to find an appropriate core region and its location for each application to optimize the communication latency and computation performance; during this step, focus is placed on reducing the fragmentation of the free core regions resulting from the task migration. Experimental results have confirmed that our approach achieves over 50% reduction in total response time when compared to recently proposed thermal-aware runtime task migration approachess.

Reinforcement Learning for Thermal and Reliability Management in Manycore Systems

“…Subsequently, the MPE directs the source PE to transfer the task's code and data to the target PE (7). Once received by the target PE ( 8), the OS allocates the task, and the PE notifies the MPE of the successful migration with a forward complete packet (9). Finally, the application resumes through a task resume packet sent to every application task.…”

“…The described patterning approach, while effective in reducing hotspots, introduces disadvantages. These include the under-utilization of the system's resources [9] and an increase in hop count between tasks [10], leading to higher application latency. Thus, these limitations motivated researchers to explore alternative strategies.…”

AMA: An Ageing Task Migration Aware for High-Performance Computing

“…Wen et al [37] proposed a dynamic task migration scheme that employs three migration modes. The migration modes are selected based on application characteristics.…”

Selective Noise Based Power-Efficient and Effective Countermeasure against Thermal Covert Channel Attacks in Multi-Core Systems

“…The last issue is one method of modelling the physical performance of the thermal leakage by analysing the system as an RC network [38]. This network acts similar to a low-pass filter with a cut-off frequency somewhere in the kilohertz range.…”