A method is provided for modifying an image. The method comprises displaying an image, the image comprising a portion of an object; and determining if an edge of the object is in a location within the portion. The method further comprises detecting movement, in a member direction, of an operating member with respect to the edge. The method still further comprises moving, if the edge is not in the location, the object in an object direction corresponding to the detected movement; and modifying, if the edge is in the location, the image in response to the detected movement, the modified image comprising the edge in the location.

Information processing apparatus, information processing method and program

Single-ISA heterogeneous multi-core processors are typically composed of small (e.g., in-order) power-efficient cores and big (e.g., out-of-order) high-performance cores. The effectiveness of heterogeneous multi-cores depends on how well a scheduler can map workloads onto the most appropriate core type. In general, small cores can achieve good performance if the workload inherently has high levels of ILP. On the other hand, big cores provide good performance if the workload exhibits high levels of MLP or requires the ILP to be extracted dynamically. This paper proposes Performance Impact Estimation (PIE) as a mechanism to predict which workload-to-core mapping is likely to provide the best performance. PIE collects CPI stack, MLP and ILP profile information, and estimates performance if the workload were to run on a different core type. Dynamic PIE adjusts the scheduling at runtime and thereby exploits fine-grained time-varying execution behavior. We show that PIE requires limited hardware support and can improve system performance by an average of 5.5% over recent state-of-the-art scheduling proposals and by 8.7% over a sampling-based scheduling policy.

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Scheduling heterogeneous multi-cores through Performance Impact Estimation (PIE)

Device scaling and large-scale integration have led to growing concerns about soft errors in microprocessors. To date, in all but the most demanding applications, implementing parity and ECC for caches and other large, regular SRAM structures have been sufficient to stem the growing soft error tide. This will not be the case for long and questions remain as to the best way to detect and recover from soft errors in the remainder of the processor - in particular, the less structured execution core. In this work, we propose the ReStore architecture, which leverages existing performance enhancing checkpointing hardware to recover from soft error events in a low cost fashion. Error detection in the ReStore architecture is novel: symptoms that hint at the presence of soft errors trigger restoration of a previous checkpoint. Example symptoms include exceptions, control flow misspeculations, and cache or translation look-aside buffer misses. Compared to conventional soft error detection via full replication, the ReStore framework incurs little overhead, but sacrifices some amount of error coverage. These attributes make it an ideal means to provide very cost effective error coverage for processor applications that can tolerate a nonzero, but small, soft error failure rate. Our evaluation of an example ReStore implementation exhibits a 2times increase in MTBF (mean time between failures) over a standard pipeline with minimal hardware and performance overheads. The MTBF increases by 20times if ReStore is coupled with protection for certain particularly vulnerable pipeline structures

ReStore: Symptom-Based Soft Error Detection in Microprocessors

Device scaling and large scale integration have led to growing concerns about soft errors in microprocessors. To date, in all but the most demanding applications, implementing parity and ECC for caches and other large, regular SRAM structures have been sufficient to stem the growing soft error tide. This will not be the case for long, and questions remain as to the best way to detect and recover from soft errors in the remainder of the processor - in particular, the less structured execution core. In this work, we propose the ReStore architecture, which leverages existing performance enhancing checkpointing hardware to recover from soft error events in a low cost fashion. Error detection in the ReStore architecture is novel: symptoms that hint at the presence of soft errors trigger restoration of a previous checkpoint. Example symptoms include exceptions, control flow mis-speculations, and cache or translation look-aside buffer misses. Compared to conventional soft error detection via full replication, the ReStore framework incurs little overhead, but sacrifices some amount of error coverage. These attributes make it an ideal means to provide very cost effective error coverage for processor applications that can tolerate a nonzero, but small, soft error failure rate. Our evaluation of an example ReStore implementation exhibits a 2x increase in MTBE (mean time between failures) over a standard pipeline with minimal hardware and performance overheads. The MTBF increases by 7x if ReStore is coupled with parity protection for certain pipeline structures.

ReStore: symptom based soft error detection in microprocessors

Chip multicore processors (CMPs) have emerged as the dominant architecture choice for modern computing platforms and will most likely continue to be dominant well into the foreseeable future. As with any system, CMPs offer a unique set of challenges. Chief among them is the shared resource contention that results because CMP cores are not independent processors but rather share common resources among cores such as the last level cache (LLC). Shared resource contention can lead to severe and unpredictable performance impact on the threads running on the CMP. Conversely, CMPs offer tremendous opportunities for mulithreaded applications, which can take advantage of simultaneous thread execution as well as fast inter thread data sharing. Many solutions have been proposed to deal with the negative aspects of CMPs and take advantage of the positive. This survey focuses on the subset of these solutions that exclusively make use of OS thread-level scheduling to achieve their goals. These solutions are particularly attractive as they require no changes to hardware and minimal or no changes to the OS. The OS scheduler has expanded well beyond its original role of time-multiplexing threads on a single core into a complex and effective resource manager. This article surveys a multitude of new and exciting work that explores the diverse new roles the OS scheduler can successfully take on.

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Survey of scheduling techniques for addressing shared resources in multicore processors

The advent of diminutive technology feature sizes has led to escalating transistor densities. Burgeoning transistor counts are casting a dark shadow on modern chip design: global interconnect delays are dominating gate delays and affecting overall system performance. Networks-on-Chip (NoC) are viewed as a viable solution to this problem because of their scalability and optimized electrical properties. However, on-chip routers are susceptible to another artifact of deep submicron technology, Process Variation (PV). PV is a consequence of manufacturing imperfections, which may lead to degraded performance and even erroneous behavior. In this work, we present the first comprehensive evaluation of NoC susceptibility to PV effects, and we propose an array of architectural improvements in the form of a new router design-called SturdiSwitch-to increase resiliency to these effects. Through extensive reengineering of critical components, SturdiSwitch provides increased immunity to PV while improving performance and increasing area and power efficiency.

On the Effects of Process Variation in Network-on-Chip Architectures

Almost all hardware platforms to date have been homogeneous with one or more identical processors managed by the operating system (OS). However, recently, it has been recognized that power constraints and the need for domain-specific high performance computing may lead architects towards building heterogeneous architectures and platforms in the near future. In this paper, we consider the three types of heterogeneous core architectures: (a) Virtual asymmetric cores: multiple processors that have identical core micro-architectures and ISA but each running at a different frequency point or perhaps having a different cache size, (b) Physically asymmetric cores: heterogeneous cores, each with a fundamentally different microarchitecture (in-order vs. out-of-order for instance) running at similar or different frequencies, with identical ISA and (c) Hybrid cores: multiple cores, where some cores have tightly-coupled hardware accelerators or special functional units. We show case studies that highlight why existing OS and hardware interaction in such heterogeneous architectures is inefficient and causes loss in application performance, throughput efficiency and lack of quality of service. We then discuss hardware and software support needed to address these challenges in heterogeneous platforms and establish efficient heterogeneous environments for platforms in the next decade. In particular, we will outline a monitoring and prediction framework for heterogeneity along with software support to take advantage of this information. Based on measurements on real platforms, we will show that these proposed techniques can provide significant advantage in terms of performance and power efficiency in heterogeneous platforms.

Efficient interaction between OS and architecture in heterogeneous platforms

Embodiments of systems, apparatuses, and methods for energy-efficient operation of a device are described. In some embodiments, a cache performance indicator of a cache is monitored, and a set of one or more cache performance parameters based on the cache performance indicator is determined. The cache is dynamically resized to an optimal cache size based on a comparison of the cache performance parameters to their energy-efficient targets to reduce power consumption.

Method, apparatus, and system for energy efficiency and energy conservation including dynamic C0-state cache resizing

Many data centers employ server consolidation to maximize the efficiency of platform resource usage. As a result, multiple virtual machines (VMs) simultaneously run on each data center platform. Contention for shared resources between these virtual machines has an undesirable and non-deterministic impact on their performance behavior in such platforms. This paper proposes the use of shared resource monitoring to (a) understand the resource usage of each virtual machine on each platform, (b) collect resource usage and performance across different platforms to correlate implications of usage to performance, and (c) migrate VMs that are resource-constrained to improve overall data center throughput and improve Quality of Service (QoS). We focus our efforts on monitoring and addressing shared cache contention and propose a new optimization metric that captures the priority of the VM and the overall weighted throughput of the data center. We conduct detailed experiments emulating data center scenarios including on-line transaction processing workloads (based on TPC-C) middle-tier workloads (based on SPECjbb and SPECjAppServer) and financial workloads (based on PARSEC). We show that monitoring shared resource contention (such as shared cache) is highly beneficial to better manage throughput and QoS in a cloud-computing data center environment.

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Shared Resource Monitoring and Throughput Optimization in Cloud-Computing Datacenters

Methods and apparatus to schedule applications in heterogeneous multiprocessor computing platforms are described. In one embodiment, information regarding performance (e.g., execution performance and/or power consumption performance) of a plurality of processor cores of a processor is stored (and tracked) in counters and/or tables. Logic in the processor determines which processor core should execute an application based on the stored information. Other embodiments are also claimed and disclosed.

Sadagopan Srinivasan

Papers

On the Effects of Process Variation in Network-on-Chip Architectures

Efficient interaction between OS and architecture in heterogeneous platforms

Method, apparatus, and system for energy efficiency and energy conservation including dynamic C0-state cache resizing

Shared Resource Monitoring and Throughput Optimization in Cloud-Computing Datacenters

Application scheduling in heterogeneous multiprocessor computing platforms