Basic block distribution analysis to find periodic behavior and simulation points in applications

Sherwood, Timothy; Perelman, Erez; Calder, Brad

doi:10.1109/pact.2001.953283

Cited by 359 publications

(335 citation statements)

References 10 publications

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“…Table 1 shows the end of the initialization phase and the beginning of the trace in millions of instructions. The end of the initialization phase was determined using the Basic Block Distribution Analysis tool [10].…”

Section: Methodsmentioning

confidence: 99%

An EPIC Processor with Pending Functional Units

Carter

Chuang

Calder

2002

Lecture Notes in Computer Science

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The Itanium processor, an implementation of an Explicitly Parallel Instruction Computing (EPIC) architecture, is an in-order processor that fetches, executes, and forwards results to functional units inorder. The architecture relies heavily on the compiler to expose Instruction Level Parallelism (ILP) to avoid stalls created by in-order processing.The goal of this paper is to examine, in small steps, changing the in-order Itanium processor model to allow execution to be performed out-of-order. The purpose is to overcome memory and functional unit latencies. To accomplish this, we consider an architecture with Pending Functional Units (PFU). The PFU architecture assigns/schedules instructions to functional units in-order. Instructions sit at the pending functional units until their operands become ready and then execute out-of-order. While an instruction is pending at a functional unit, no other instruction can be scheduled to that functional unit. We examine several PFU architecture designs. The minimal design does not perform renaming, and only supports bypassing of non-speculative result values. We then examine making PFU more aggressive by supporting speculative register state, and then finally by adding in register renaming. We show that the minimal PFU architecture provides on average an 18% speedup over an in-order EPIC processor and produces up to half of the speedup that would be gained using a full out-of-order architecture.

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Section: Methodsmentioning

confidence: 99%

An EPIC Processor with Pending Functional Units

Carter

Chuang

Calder

2002

Lecture Notes in Computer Science

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“…In [62], Phansalkar et al use k-means clustering (as described in [72]) to predict a software program's performance. They use five categories of benchmarks to measure similarity between programs: Instruction Mix, Behavior of branches, Inherent Instruction Level Parallelism, Data locality and Instruction locality.…”

Section: Clusteringmentioning

confidence: 99%

“…To reduce the dimensionality of data (29 metrics), they use Principal Component Analysis which transforms the data to a new coordinate system. They find the optimal number of clusters by using the Bayesian Information Criterion a described in [72]. Once they identify what cluster the target program goes into, they use the performance measure of the closest program to the center of the cluster as the prediction.…”

Section: Clusteringmentioning

confidence: 99%

An effort prediction framework for software defect correction

Hassouna¹,

Tahvildari²

2010

Information and Software Technology

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Developers apply changes and updates to software systems to adapt to emerging environments and address new requirements. In turn, these changes introduce additional software defects, usually caused by our inability to comprehend the full scope of the modified code. As a result, software practitioners have developed tools to aid in the detection and prediction of imminent software defects, in addition to the effort required to correct them. Although software development effort prediction has been in use for many years, research into defect-correction effort prediction is relatively new. The increasing complexity, integration and ubiquitous nature of current software systems has sparked renewed interest in this field. Effort prediction now plays a critical role in the planning activities of managers. Accurate predictions help corporations budget, plan and distribute available resources effectively and efficiently. In particular, early defect-correction effort predictions could be used by testers to set schedules, and by managers to plan costs and provide earlier feedback to customers about future releases.In this work, we address the problem of predicting the effort needed to resolve a software defect. More specifically, our study is concerned with defects or issues that are reported on an Issue Tracking System or any other defect repository. Current approaches use one prediction method or technique to produce effort predictions. This approach usually suffers from the weaknesses of the chosen prediction method, and consequently the accuracy of the predictions are affected. To address this problem, we present a composite prediction framework. Rather than using one prediction approach for all defects, we propose the use of multiple integrated methods which complement the weaknesses of one another. Our framework is divided into two sub-categories, Similarity-Score Dependent and Similarity-Score Independent. The Similarity-Score Dependent method utilizes the power of Case-Based Reasoning, also known as Instance-Based Reasoning, to compute predictions. It relies on matching target issues to similar historical cases, then combines their known effort for an informed estimate. On the other hand, the Similarity-Score Independent method makes use of other defect-related information with some statistical manipulation to produce the required estimate. To measure similarity between defects, some method of distance calculation must be used. In some cases, this method might produce misleading results due to observed inconsistencies in history, and the fact that current similarity-scoring techniques cannot account for all the variability in the data. In this case, the Similarity-Score Independent method can be used to estimate the effort, where the effect of such inconsistencies can be reduced.We have performed a number of experimental studies on the proposed framework to assess the effectiveness of the presented techniques. We extracted the data sets from an operational Issue Tracking System in order to test the validity...

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“…Some of them use control-flow information [15,16,14,11,12], such as counts of executed instructions, basic blocks, loops, or functions, as the fingerprint of program execution. This fingerprint depends on the executed source code.…”

Section: Related Workmentioning

confidence: 99%

“…SimPoint [15,16] partitions a program execution into intervals with the same number of instructions and identifies the phases based on the BBV of each interval. One interval, called a simpoint, is selected as the representative of its phase.…”

Section: Related Workmentioning

confidence: 99%

Efficient Program Power Behavior Characterization

Jiménez

Kremer

2007

High Performance Embedded Architectures and Compilers

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Abstract. Fine-grained program power behavior is useful in both evaluating power optimizations and observing power optimization opportunities. Detailed power simulation is time consuming and often inaccurate. Physical power measurement is faster and objective. However, fine-grained measurement generates enormous amounts of data in which locating important features is difficult, while coarse-grained measurement sacrifices important detail. We present a program power behavior characterization infrastructure that identifies program phases, selects a representative interval of execution for each phase, and instruments the program to enable precise power measurement of these intervals to get their time-dependent power behavior. We show that the representative intervals accurately model the fine-grained time-dependent behavior of the program. They also accurately estimate the total energy of a program. Our compiler infrastructure allows for easy mapping between a measurement result and its corresponding source code. We improve the accuracy of our technique over previous work by using edge vectors, i.e., counts of traversals of control-flow edges, instead of basic block vectors, as well as incorporating event counters into our phase classification. We validate our infrastructure through the physical power measurement of 10 SPEC CPU 2000 integer benchmarks on an Intel Pentium 4 system. We show that using edge vectors reduces the error of estimating total program energy by 35% over using basic block vectors, and using edge vectors plus event counters reduces the error of estimating the fine-grained time-dependent power profile by 22% over using basic block vectors.

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Basic block distribution analysis to find periodic behavior and simulation points in applications

Abstract: Abstract

Cited by 359 publications

References 10 publications

An EPIC Processor with Pending Functional Units

An EPIC Processor with Pending Functional Units

An effort prediction framework for software defect correction

Efficient Program Power Behavior Characterization

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