Many sequential applications are difficult to parallelize because of unpredictable control flow, indirect data access, and inputdependent parallelism. These difficulties led us to build a software system for behavior oriented parallelization (BOP), which allows a program to be parallelized based on partial information about program behavior, for example, a user reading just part of the source code, or a profiling tool examining merely one or few executions.The basis of BOP is programmable software speculation, where a user or an analysis tool marks possibly parallel regions in the code, and the run-time system executes these regions speculatively. It is imperative to protect the entire address space during speculation. The main goal of the paper is to demonstrate that the general protection can be made cost effective by three novel techniques: programmable speculation, critical-path minimization, and value-based correctness checking. On a recently acquired multicore, multi-processor PC, the BOP system reduced the end-to-end execution time by integer factors for a Lisp interpreter, a data compressor, a language parser, and a scientific library, with no change to the underlying hardware or operating system. Dedication This paper is dedicated to the memory of Ken Kennedy, who passed away on February 7, for his lasting leadership in parallel computing research.
On modern computer systems, the memory performance of an application depends on its locality. For a single execution, locality-correlated measures like average miss rate or working-set size have long been analyzed using reuse distance-the number of distinct locations accessed between consecutive accesses to a given location. This article addresses the analysis problem at the program level, where the size of data and the locality of execution may change significantly depending on the input.The article presents two techniques that predict how the locality of a program changes with its input. The first is approximate reuse-distance measurement, which is asymptotically faster than exact methods while providing a guaranteed precision. The second is statistical prediction of locality in all executions of a program based on the analysis of a few executions. The prediction process has three steps: dividing data accesses into groups, finding the access patterns in each group, and building parameterized models. The resulting prediction may be used on-line with the help of distance-based sampling. When evaluated on fifteen benchmark applications, the new techniques predicted program locality with good accuracy, even for test executions that are orders of magnitude larger than the training executions.The two techniques are among the first to enable quantitative analysis of whole-program locality in general sequential code. These findings form the basis for a unified understanding of program Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org. locality and its many facets. Concluding sections of the article present a taxonomy of related literature along five dimensions of locality and discuss the role of reuse distance in performance modeling, program optimization, cache and virtual memory management, and network traffic analysis.
Abstract-Fast track is a software speculation system that enables unsafe optimization of sequential code. It speculatively runs optimized code to improve performance and then checks the correctness of the speculative code by running the original program on multiple processors.We present the interface design and system implementation for Fast Track. It lets a programmer or a profiling tool mark fast-track code regions and uses a run-time system to manage the parallel execution of the speculative process and its checking processes and ensures the correct display of program outputs. The core of the run-time system is a novel concurrent algorithm that balances exploitable parallelism and available processors when the fast track is too slow or too fast. The programming interface closely affects the run-time support. Our system permits both explicit and implicit end markers for speculatively optimized code regions as well as extensions that allow the use of multiple tracks and user defined correctness checking. We discuss the possible uses of speculative optimization and demonstrate the effectiveness of our prototype system by examples of unsafe semantic optimization and a general system for fast memory-safety checking, which is able to reduce the checking time by factors between 2 and 7 for large sequential code on a 8-CPU system.
Intrinsic self-healing materials possessing the capability to autonomously repair their structure and functionalities upon mechanical damage have attracted great attention.
Measuring the indirect cost of context switch is a challenging problem. In this paper, we show our results of experimentally quantifying the indirect cost of context switch using a synthetic workload. Specifically, we measure the impact of program data size and access stride on context switch cost. We also demonstrate the potential impact of OS background interrupt handling on the measurement accuracy. Such impact can be alleviated by using a multi-processor system on which one processor is employed for context switch measurement while the other runs OS background tasks.
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