Evaluating Orthogonality between Application Auto-Tuning and Run-Time Resource Management for Adaptive OpenCL Applications

Abstract-The ever increasing number of processing units integrated on the same many-core chip delivers computational power that can exceed the performance requirements of a single application. The number of chips (and related power consumption) can thus be reduced to serve multiple applications -a practice which is called resource consolidation. However, this solution requires techniques to partition and assign resources among the applications and to manage unpredictable dynamic workloads. To provide the performance requirements in such scenarios, we exploit application auto-tuning, based on design-time analysis, of both application-specific dynamic knobs and computational parallelism. Such features are implemented in a software library, which is used to demonstrate the main contribution of this paper: a light-weight Run-Time Resource Management -RTRM -technique to improve resource sharing for computationally intensive OpenCL applications. We evaluate how much the interaction between RTRM and application auto-tuning can become synergistic yet orthogonal. In the proposed approach, run-time adaptation decisions are taken by each application, autonomously. This has two main advantages: i) a non-invasive application design, in terms of source code, and ii) a very low run-time overhead, since it does not require any central coordination of a supervisor nor communication between the applications. We carried out an experimental campaign by using a video processing application -an OpenCL stereo-matching implementation -and stressing out resource usage. We proved that, while RTRM is necessary to provide lower variance of the application performance, the application auto-tuning layer is fundamental to trade it off with respect to the computation accuracy

マルチメディアアプリケーションのためのベクトルアーキテクチャ設計

Tohoku University小林広明課

Run-time resource management based on design space exploration

A main challenge in today's embedded system design is to find the perfect balance between performance and power consumption. This paper presents a run-time resource management framework for embedded heterogeneous multi-core platforms. It allows dynamic adaptation to changing application context and transparent optimization of the platform resource usage following a distributed and hierarchical approach. A Global Resource Manager (GRM) is running in parallel with the central manager of the application on the host processor of the platform. Each IP core of the platform can execute its own Local Resource Manager (LRM), and the GRM conforms to practices of each LRM. The operating points managed by the GRM are identified in a design-space exploration phase as a set of Pareto-optimal configurations of the application and their impacts with regards to the quality of experience, performance and energy consumption. The GRM has already been integrated in a POSIX version of an audio-driven video surveillance application in order to maximize its QoE parameters with respect to the battery duration and the energy budget of the platform, used to analyze the GRM efficiency