Selecting Software Phase Markers with Code Structure Analysis

Lau, Jeremy; Perelman, Erez; Calder, Brad

doi:10.1109/cgo.2006.32

Cited by 45 publications

(42 citation statements)

References 29 publications

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“…Wang et al present a loop selection algorithm and its performance estimation [28]. Lau et al used the call-loop graph to detect the phase change of code execution [29]. However, the authors of these papers do not discuss the details of how they obtained the results on profiling.…”

Section: Related Workmentioning

confidence: 99%

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Identifying Program Loop Nesting Structures during Execution of Machine Code

Sato

Inoguchi

Nakamura

2014

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents a mechanism for detecting dynamic loop and procedure nesting during the actual program execution on-the-fly. This mechanism aims primarily at making better strategies for performance tuning or parallelization. Using a pre-compiled application executable machine code as an input, our mechanism statically generates simple but precise markers that indicate loop entries and loop exits, and dynamically monitors loop nesting that appears during the actual execution together with call context tree. To keep precise loop structures all the time, we monitor the indirect jumps that enter the loop regions and the setjmp/longjmp functions that cause irregular function call transfers. We also present a novel representation called Loop-Call Context Graph that can keep track of inter-procedural loop nests. We implement our mechanism and evaluate it using SPEC CPU2006 benchmark suite. The results confirm that our mechanism can successfully reveal the precise inter-procedural loop nest structures from all of SPEC CPU2006 benchmark executions without any particular compiler support. The results also show that it can reduce runtime loop detection overheads compared with the existing loop profiling method. key words: dynamic loop nests, loop-call context tree, on-the-fly loop detection

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Section: Related Workmentioning

confidence: 99%

“…In several papers, the loop-call graph is utilized to represent nested loop structures [7], [29]. Like the difference between call graph and CCT, the loop-call graph cannot depict exact paths of nested loops and procedure calls due to merging of the flow.…”

Section: Related Workmentioning

confidence: 99%

Identifying Program Loop Nesting Structures during Execution of Machine Code

Sato

Inoguchi

Nakamura

2014

IEICE Trans. Inf. & Syst.

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“…Lau et al proposed an automated profiling approach to identify code locations whose executions correlate with phase changes [10]. They called these code constructs software phase markers, which are used to detect phase changes across different inputs to a program without hardware support.…”

Section: Software Phase Markermentioning

confidence: 99%

“…Fixed-length intervals can degrade the estimation accuracy because a single phase can have phase changes [10]- [12].…”

Section: Fixed-length Intervalmentioning

confidence: 99%

An Inductive Method to Select Simulation Points

MinSeong

Fukuda

Goshima

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYThe time taken for processor simulation can be drastically reduced by selecting simulation points, which are dynamic sections obtained from the simulation result of processors. The overall behavior of the program can be estimated by simulating only these sections. The existing methods to select simulation points, such as SimPoint, used for selecting simulation points are deductive and based on the idea that dynamic sections executing the same static section of the program are of the same phase. However, there are counterexamples for this idea. This paper proposes an inductive method, which selects simulation points from the results obtained by pre-simulating several processors with distinctive microarchitectures, based on assumption that sections in which all the distinctive processors have similar istructions per cycle (IPC) values are of the same phase. We evaluated the first 100G instructions of SPEC 2006 programs. Our method achieved an IPC estimation error of approximately 0.1% by simulating approximately 0.05% of the 100G instructions.

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“…For instance, Georges et al selected Java methods whose behavior variation is relatively small [10]. There are also many algorithms which exploit aspects of the program structure such as loops and procedures [4,10,13,16], regions [12], and call sites [15] to determine phase boundaries. Figure 1 shows the phase information for a run of pseudoJBB with a 512MB heap.…”

Section: Behavior Phases In Programsmentioning

confidence: 99%

Program-level adaptive memory management

Zhang

Kelsey

Shen

et al. 2006

Proceedings of the 5th International Symposium on Memory Management

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Most application's performance is impacted by the amount of available memory. In a traditional application, which has a fixed working set size, increasing memory has a beneficial effect up until the application's working set is met. In the presence of garbage collection this relationship becomes more complex. While increasing the size of the program's heap reduces the frequency of collections, collecting a heap with memory paged to the backing store is very expensive. We first demonstrate the presence of an optimal heap size for a number of applications running on a machine with a specific configuration. We then introduce a scheme which adaptively finds this good heap size. In this scheme, we track the memory usage and number of page faults at a program's phase boundaries. Using this information, the system selects the soft heap size. By adapting itself dynamically, our scheme is independent of the underlying main memory size, code optimizations, and garbage collection algorithm. We present several experiments on real applications to show the effectiveness of our approach. Our results show that program-level heap control provides up to a factor of 7.8 overall speedup versus using the best possible fixed heap size controlled by the virtual machine on identical garbage collectors.

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Selecting Software Phase Markers with Code Structure Analysis

Cited by 45 publications

References 29 publications

Identifying Program Loop Nesting Structures during Execution of Machine Code

Identifying Program Loop Nesting Structures during Execution of Machine Code

An Inductive Method to Select Simulation Points

Program-level adaptive memory management

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