Rob C. Knauerhase

An Analysis of Performance Interference Effects in Virtual Environments

Virtualization is an essential technology in modern datacenters. Despite advantages such as security isolation, fault isolation, and environment isolation, current virtualization techniques do not provide effective performance isolation between virtual machines (VMs). Specifically, hidden contention for physical resources impacts performance differently in different workload configurations, causing significant variance in observed system throughput. To this end, characterizing workloads that generate performance interference is important in order to maximize overall utility. In this paper, we study the effects of performance interference by looking at system-level workload characteristics. In a physical host, we allocate two VMs, each of which runs a sample application chosen from a wide range of benchmark and real-world workloads. For each combination, we collect performance metrics and runtime characteristics using an instrumented Ken hypervisor. Through subsequent analysis of collected data, we identify clusters of applications that generate certain types of performance interference. Furthermore, we develop mathematical models to predict the performance of a new application from its workload characteristics. Our evaluation shows our techniques were able to predict performance with average error of approximately 5%

Operating system support for overlapping-ISA heterogeneous multi-core architectures

A heterogeneous processor consists of cores that are asymmetric in performance and functionality. Such a design provides a cost-effective solution for processor manufacturers to continuously improve both single-thread performance and multi-thread throughput. This design, however, faces significant challenges in the operating system, which traditionally assumes only homogeneous hardware. This paper presents a comprehensive study of OS support for heterogeneous architectures in which cores have asymmetric performance and overlapping, but non-identical instruction sets. Our algorithms allow applications to transparently execute and fairly share different types of cores. We have implemented these algorithms in the Linux 2.6.24 kernel and evaluated them on an actual heterogeneous platform. Evaluation results demonstrate that our designs efficiently manage heterogeneous hardware and enable significant performance improvements for a range of applications.

Runnemede: An architecture for Ubiquitous High-Performance Computing

DARPAs Ubiquitous High-Performance Computing (UHPC) program asked researchers to develop computing systems capable of achieving energy efficiencies of 50 GOPS/Watt, assuming 2018-era fabrication technologies. This paper describes Runnemede, the research architecture developed by the Intel-led UHPC team. Runnemede is being developed through a co-design process that considers the hardware, the runtime/OS, and applications simultaneously. Near-threshold voltage operation, fine-grained power and clock management, and separate execution units for runtime and application code are used to reduce energy consumption. Memory energy is minimized through application-managed on-chip memory and direct physical addressing. A hierarchical on-chip network reduces communication energy, and a codelet-based execution model supports extreme parallelism and fine-grained tasks. We present an initial evaluation of Runnemede that shows the design process for our on-chip network, demonstrates 2-4x improvements in memory energy from explicit control of on-chip memory, and illustrates the impact of hardware-software co-design on the energy consumption of a synthetic aperture radar algorithm on our architecture.

Kinship: efficient resource management for performance and functionally asymmetric platforms

On-chip heterogeneity has become key to balancing performance and power constraints, resulting in disparate (functionally overlapping but not equivalent) cores on a single die. Requiring developers to deal with such heterogeneity can impede adoption through increased programming effort and result in cross-platform incompatibility. We propose that systems software must evolve to dynamically accommodate heterogeneity and to automatically choose task-to-resource mappings to best use these features. We describe the kinship approach for mapping workloads to heterogeneous cores. A hypervisor-level realization of the approach on a variety of experimental heterogeneous platforms demonstrates the general applicability and utility of kinship-based scheduling, matching dynamic workloads to available resources as well as scaling with the number of processes and with different types/configurations of compute resources. Performance advantages of kinship based scheduling are evident for runs across multiple generations of heterogeneous platforms.

High-performance, power-aware computing - HPPAC

High-performance computing is and has always been performance-oriented. However, a consequence of the push towards maximum performance is increased energy consumption, especially in datacenters and supercomputing centers. Moreover, as peak performance is rarely attained, some of this energy consumption results in little or no performance gain. In addition, large energy consumption costs datacenters and supercomputing centers a significant amount of money and wastes natural resources.