Non-intrusive dynamic application profiler for detailed loop execution characterization

Nair, A. P.; Lysecky, Roman

doi:10.1145/1450095.1450102

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…The jump address starts a new SuperBlock. This is similar to the method used by Warp [2] and by the Dynamic Application Profiler [6], which also uses backward branches, but in their case they detect the complete loop, including the control-flow.…”

Section: Identifying Trace Segmentsmentioning

confidence: 98%

On Identifying Segments of Traces for Dynamic Compilation

Bispo

Cardoso

2010

2010 International Conference on Field Programmable Logic and Applications

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Typical computing systems based on general purpose processors (GPPs) are extended with coarse-grained reconfigurable arrays (CGRAs) to provide higher performance and/or energy savings. In order for applications to take advantage of these computing systems, efficient dynamic mapping techniques are required. Those dynamic mapping techniques will be responsible for automatically moving computations originally running in the GPP to the CGRA. The concept of dynamic compilation, widespread in the context of JIT compilation to GPPs, is receiving more attention by the reconfigurable computing community. This paper presents our approach to dynamically map computations to CGRAs coupled to a GPP. Specifically, we present the identification of large sequences of instructions, MegaBlocks, being executed in a GPP. These MegaBlocks are then mapped to the target CGRA. We evaluate the potential of the MegaBlocks over Basic Blocks and SuperBlocks to increase the IPC when targeting a CGRA and considering the execution of a number of representative benchmarks.

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Section: Identifying Trace Segmentsmentioning

confidence: 98%

On Identifying Segments of Traces for Dynamic Compilation

Bispo

Cardoso

2010

2010 International Conference on Field Programmable Logic and Applications

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“…In Section 2, we provide a comprehensive overview of existing software and hardware based profiling methods, discuss how such approaches can potentially change the behavior of the application and incur significant runtime overhead, and specifically highlight the nonintrusive, frequent loop detection profiler presented in Gordon-Ross and Vahid [2005]. In Section 3, we present a detailed overview of our original DAProf design presented in Nair and Lysecky [2008] that profiles an application by only monitoring short backward branches and provides detailed information regarding loop execution behavior, including the breakdown of loop executions versus average iterations per execution-providing both additional profiling information and improved accuracy compared to the frequent loop detection profiler. In Section 4, we introduce the problem of function call interference that can result in inaccurate profiling and present our extended DAProf design with support for monitoring function calls and function returns that overcomes the problem of function call interference.…”

Section: S Hw/sw = T Sw T Hw/swmentioning

confidence: 99%

“…Figure 1 presents an overview of our original dynamic application profiler [Nair and Lysecky 2008] design, highlighting its integration within a microprocessor-based system and internal profiling architecture. DAProf nonintrusively monitors the microprocessor's instruction bus to determine the address of the currently executed instruction whenever a short backward branch is executed.…”

Section: Dynamic Application Profilermentioning

confidence: 99%

Efficient hardware-based nonintrusive dynamic application profiling

Nair

Shankar

Lysecky

2011

ACM Trans. Embed. Comput. Syst.

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Application profiling-the process of monitoring an application to determine the frequency of execution within specific regions-is an essential step within the design process for many software and hardware systems. Profiling is often a critical step within hardware/software partitioning utilized to determine the critical kernels of an application. In this article, we present an innovative, nonintrusive dynamic application profiler (DAProf) capable of profiling an executing application by monitoring the application's short backward branches, function calls, and function returns. The resulting profile information provides an accurate characterization of the frequently executed loops within the application providing a breakdown of loop executions versus loop iterations per execution. DAProf achieves excellent profiling accuracy with an average accuracy of 98% for loop executions, 97% for average iterations per execution, and 95% for percentage of execution time. In addition, the presented dynamic application profiler incurs as little as 11% area overhead compared to an ARM9 microprocessor. DAProf is ideally suited for rapidly profiling software applications and dynamic optimization approaches such as dynamic hardware/software partitioning in which detailed loop execution information is needed to provide accurate performance estimates.

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“…In the case of real-time systems, which are usually designed with very tight timing constraints, the slightest run time overhead can lead to missed deadlines and potential system failure. For further details and discussion of existing software and hardware based profiling techniques, we refer the interested reader to [16] [18].…”

Section: Introductionmentioning

confidence: 99%

“…In [16], we presented an efficient, non-intrusive dynamic application profiler (DAProf) capable of profiling an executing application by monitoring the application's short backwards branches and providing detailed profiling statistics for characterizing loop execution behavior, including loop executions, average iterations per execution, and percentage of execution time. However, DAProf was originally designed to profile single task applications.…”

Section: Introductionmentioning

confidence: 99%