Modern computing paradigms, such as cloud computing, are increasingly adopting GPUs to boost their computing capabilities primarily due to the heterogeneous nature of AI/ML/deep learning workloads. However, the energy consumption of GPUs is a critical problem. Dynamic Voltage Frequency Scaling (DVFS) is a widely used technique to reduce the dynamic power of GPUs. Yet, configuring the optimal clock frequency for essential performance requirements is a non-trivial task due to the complex nonlinear relationship between the application's runtime performance characteristics, energy, and execution time. It becomes more challenging when different applications behave distinctively with similar clock settings. Simple analytical solutions and standard GPU frequency scaling heuristics fail to capture these intricacies and scale the frequencies appropriately. In this regard, we propose a data-driven frequency scaling technique by predicting the power and execution time of a given application over different clock settings. We collect the data from application profiling and train the models to predict the outcome accurately. The proposed solution is generic and can be easily extended to different kinds of workloads and GPU architectures. Furthermore, using this frequency scaling by prediction models, we present a deadline-aware application scheduling algorithm to reduce energy consumption while simultaneously meeting their deadlines. We conduct real extensive experiments on NVIDIA GPUs using several benchmark applications. The experiment results have shown that our prediction models have high accuracy with the average RMSE values of 0.38 and 0.05 for energy and time prediction, respectively. Also, the scheduling algorithm consumes 15.07% less energy as compared to the baseline policies.

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.

Silicon Design and Electronic Design Automation (EDA) business is highly competitive and time to market is of utmost importance in the semiconductor industry where companies put in a lot of effort to make sure that the first silicon is as healthy as possible. Hence it is imperative that the EDA compute environment provides maximum uptime to design engineers by utilizing several different High Performance Computing (HPC) technologies. In this paper we present Intel's EDA compute infrastructure, along with a detailed software and system architecture for supporting workload checkpointing, restoration and migration for specifically EDA jobs that are interactive in nature. We also describe our experiences in providing high availability for such EDA applications using existing popular HA/FT techniques. We believe that this is one of the few detailed descriptions of the EDA compute infrastructure of a large and complex semiconductor design company and that this will be useful in addressing future HPC challenges for EDA workloads as HPC technologies mature and evolve.

High-performance parallel computing architectures are increasingly based on multi-core processors. While current commercially available processors are at 8 and 16 cores, technological and power constraints are limiting the performance growth of the cores and are resulting in architectures with much higher core counts, such as the experimental many-core Intel Single-chip Cloud Computer (SCC) platform. These trends are presenting new sets of challenges to HPC applications including programming complexity and the need for extreme energy efficiency.

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 center's 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.

Abstract Grid‐enabled infrastructures and problem‐solving environments can significantly increase the scale, cost‐effectiveness and utility of scientific simulations, enabling highly accurate simulations that provide in‐depth insight into complex phenomena. This paper presents a prototype of such an environment, i.e. an interactive and collaborative problem‐solving environment for the formulation, development, deployment and management of oil reservoir and environmental flow simulations in computational Grid environments. The project builds on three independent research efforts: (1) the IPARS oil reservoir and environmental flow simulation framework; (2) the NetSolve Grid engine; and (3) the Discover Grid‐based computational collaboratory. Its primary objective is to demonstrate the advantages of an integrated simulation infrastructure towards effectively supporting scientific investigation on the Grid, and to investigate the components and capabilities of such an infrastructure. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract This paper presents the design, implementation and experimental evaluation of DIOS (Distributed Interactive Object Substrate), an interactive object infrastructure to enable the runtime monitoring, interaction and computational steering of parallel and distributed applications. DIOS enables application objects (data structures, algorithms) to be enhanced with sensors and actuators so that they can be interrogated and controlled. Application objects may be distributed (spanning many processors) and dynamic (be created, deleted, changed or migrated). Furthermore, DIOS provides a control network that interconnects the interactive objects in a parallel/distributed application and enables external discovery, interrogation, monitoring and manipulation of these objects at runtime. DIOS is currently being used to enable interactive visualization, monitoring and steering of a wide range of scientific applications, including oil reservoir, compressible turbulence and numerical relativity simulations. Copyright © 2003 John Wiley & Sons, Ltd.

Abstract This paper presents the design, implementation, and deployment of the DISCOVER Web‐based computational collaboratory. Its primary goal is to bring large distributed simulations to the scientists'/engineers' desktop by providing collaborative Web‐based portals for monitoring, interaction and control. DISCOVER supports a three‐tier architecture composed of detachable thin‐clients at the front‐end, a network of interaction servers in the middle, and a control network of sensors, actuators, and interaction agents at the back‐end. The interaction servers enable clients to connect and collaboratively interact with registered applications using a browser. The application control network enables sensors and actuators to be encapsulated within, and directly deployed with the computational objects. The application interaction gateway manages overall interaction. It uses Java Native Interface to create Java proxies that mirror computational objects and allow them to be directly accessed at the interaction server. Security and authentication are provided using customizable access control lists and SSL‐based secure servers. Copyright © 2001 John Wiley & Sons, Ltd.