Mats: MultiCore Adaptive Trace Selection

Dynamically optimizing programs is worthwhile only if the overhead created by the dynamic optimizer is less than the benefit gained from the optimization. Program trace selection is one of the most important, yet time consuming, components of many dynamic optimizers. The dynamic application of monitoring and profiling can often result in an execution slowdown rather than speedup. Achieving significant performance gain from dynamic optimization has proven to be quite challenging. However, current technological advances, namely multicore architectures, enable us to design new approaches to meet this challenge. Selecting traces in current dynamic optimizers is typically achieved through the use of instrumentation to collect control flow information from a running application. Using instrumentation for runtime analysis requires the trace selection algorithms to be light weight, and this limits how sophisticated these algorithms can be. This is problematic because the quality of the traces can determine the potential benefits that can be gained from optimizing the traces. In many cases, even when using a lightweight approach, the overhead incurred is more than the benefit of the optimizations. In this paper we exploit the multicore architecture to design an aggressive trace selection approach that produces better traces and does not perturb the running application. 1

A Survey of Phase Classification Techniques for Characterizing Variable Application Behavior

Adaptable computing is an increasingly important paradigm that specializes system resources to variable application requirements, environmental conditions, or user requirements. Adapting computing resources to variable application requirements (or application phases) is otherwise known as phase-based optimization. Phase-based optimization takes advantage of application phases, or execution intervals of an application, that behave similarly, to enable effective and beneficial adaptability. In order for phase-based optimization to be effective, the phases must first be classified to determine when application phases begin and end, and ensure that system resources are accurately specialized. In this paper, we present a survey of phase classification techniques that have been proposed to exploit the advantages of adaptable computing through phase-based optimization. We focus on recent techniques and classify these techniques with respect to several factors in order to highlight their similarities and differences. We divide the techniques by their major defining characteristics---online/offline and serial/parallel. In addition, we discuss other characteristics such as prediction and detection techniques, the characteristics used for prediction, interval type, etc. We also identify gaps in the state-of-the-art and discuss future research directions to enable and fully exploit the benefits of adaptable computing.Comment: To appear in IEEE Transactions on Parallel and Distributed Systems (TPDS

Phase-based tuning for better utilized performance-asymmetric multicores

The latest trend towards performance asymmetry among cores on a single chip of a multicore processor is posing new software engineering challenges for developers. A key challenge is that for effective utilization of these performance-asymmetric multicore processors, application threads must be assigned to cores such that the resource needs of a thread closely matches resource availability at the assigned core. Determining this assignment manually is tedious, error prone, and it significantly complicates software development. We contribute a transparent and fully-automatic program analysis, which we call phase-guided tuning, to solve this problem. Phase-guided tuning adapts an application to effectively utilize performance-asymmetric cores of a processor. Our technique does not require any changes in the compiler or operating system, thus it is easy to deploy in existing tool chains. It does not require any input from the programmer except the application. Furthermore, it is independent of the characteristics (performance-asymmetry) of the target multicore processor, which has two benefits. First, it avoids the need to create multiple customizations of the binary for each target architecture, and second it relieves the programmer of the burden of anticipating the target architecture. Last but not least, our technique significantly improves performance. Compared to the stock Linux scheduler, our best technique shows 215% improvement in throughput and 36% average process speedup, while maintaining fairness and with negligible overheads

Phase-based Tuning for Better Utilized Multicores

The latest trend towards performance asymmetry among cores on a single chip of a multicore processor is posing new software engineering challenges for developers. A key challenge is that for effective utilization of these performance-asymmetric multicore processors, code sections of a program must be assigned to cores such that the resource needs of a section closely matches resource availability at the assigned core. Determining this assignment manually is tedious, error prone, and it significantly complicates software development. We contribute a transparent and fully-automatic program analysis, which we call phase-based tuning, to solve this problem. Phase-based tuning adapts an application to effectively utilize performance-asymmetric cores of a processor. Our technique does not require any changes in the compiler or operating system, thus it is easy to deploy in existing tool chains. It does not require any input from the programmer except the application. Furthermore, it is independent of the characteristics (performance-asymmetry) of the target multicore processor, which has two benefits. First, it avoids the need to create multiple customizations of the binary for each target architecture, and second it relieves the programmer of the burden of anticipating the target architecture. Last but not least, our technique significantly improves performance. Compared to the stock Linux scheduler, our best technique shows 36% average process speedup, while maintaining fairness and with negligible overheads