Harinarayanan Seshadri

Investigating the potential of application-centric aggressive power management for HPC workloads

Energy efficiency of large-scale data centers is becoming a major concern not only for reasons of energy conservation, failures, and cost reduction, but also because such sys tems are soon reaching the limits of power available to them. Like High Performance Computing (HPC) systems, large-scale clu ster-based data centers can consume power in megawatts, and of all the power consumed by such a system, only a fraction is used for actual computations. In this paper, we study the potential of application-centric aggressive power management of data centers resources for HPC workloads. Specifically, we consider power management mechanisms and controls (currently or soon to be) available at different levels and for different subsystems, and leverage several innovative approaches that have been taken to tackle this problem in the last few years, can be effectively used in a application-aware manner for HPC workloads. To do this, we first profile sta ndard HPC benchmarks with respect to behaviors, resource usage and power impact on individual computing nodes. Based on a power and latency model and the workload profiles, we develop an algorithm that can improve energy efficiency with little or no performance loss. We then evaluate our proposed algorithm through simulations using empirical power characterization and quantification. Finally, we validate the simulation results with actual executions on real hardware. The obtained results show that by using application aware power management, we can re-du ce the average energy consumption without significant penalty in performance. This motivates us to investigate autonomic approaches for application-aware aggressive power management and cross layer and cross function predictive subsystem level power management for large-scale data centers.

On a rewriting strategy for dynamically managing power constraints and power dissipation in SoCs

We present a novel and highly automated technique for dynamic system level power management of System-on-a-Chip (SoC) designs. We present a formal system to represent power constraints and power intent as rules. We also present a Term Rewriting Systems based rule rewriting engine as our dynamic power manager. We provide a notion of formal correctness of our rule engine execution and provide a robust algorithm to dynamically and automatically manage power consumption in large SoC designs. There are two fundamental building blocks at the core of our technique. First, we present a powerful formal system to capture power constraints and power intent as rules. This is a self-checking system and will automatically flag conflicting constraints or rules. Next, we present a rewriting strategy for managing power constraint rules using a formal deductive logic technique specially honed for dynamic power management of SoC designs. Together, this provides a common platform and representation to seamlessly cooperate between hardware and software constraints to achieve maximum platform power optimization dynamically during execution. We demonstrate our technique in multiple contexts on an SoC design of the state-of-the-art next generation Intel smartphone platform.

TaskFolder: Dynamic and Fine-Grained Workload Consolidation for Mobile Devices

Despite the resource-constrained environment associated with mobile devices, the Android task scheduler tries to spread the workload equally among all CPU cores. While this is a sensible use of resources in desktop or server environments, it is inefficient for mobile devices, because they usually have lower computing demand and require single-user-perceptible performance guaran-tees. As a result, spreading tasks to all cores does not improve per-formance, increases energy consumption, and can cause perfor-mance issues when multiple mobile virtual machines attempt to engage as many cores as possible. To address this problem, we propose TaskFolder, a multi-core management scheme implemented on top of the task scheduler. TaskFolder attempts to compute the minimum number of cores required to perform the current workload without sacrificing per-formance, and schedules tasks to only that minimal number of cores. This number is called the Core Concurrency and is calculated based on past task dynamics. Experimental results of a case study show that TaskFolder saves an average of 19% and up to 48% of CPU power over a set of mobile applications on the latest Intel mobile platform.