TEST: a Tracer for Extracting Speculative Threads

Chen, M.; Olukotun, Kunle

doi:10.1109/cgo.2003.1191554

Cited by 25 publications

(26 citation statements)

References 31 publications

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“…The static analysis to gain probability information has been tried in [26], but is limited to array references with linear subscript expressions only. Recent work done by [27] also uses hardware supported profiling to help thread generation. The profiling result is again limited, compared to our approach.…”

Section: Related Workmentioning

confidence: 99%

Data Dependence Profiling for Speculative Optimizations

Chen

Lin

Dai

et al. 2004

Lecture Notes in Computer Science

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Abstract. Data dependence analysis is the foundation to many reordering related compiler optimizations and loop parallelization. Traditional data dependence analysis algorithms are developed primarily for Fortran-like subscripted array variables. They are not very effective for pointer-based references in C or C++. With more advanced hardware support for speculative execution, such as the advanced load instructions in Intel's IA64 architecture, some data dependences with low probability can be speculatively ignored. However, such speculative optimizations must be carefully applied to avoid excessive cost associated with potential mis-speculations. Data dependence profiling is one way to provide probabilistic information on data dependences to guide such speculative optimizations and speculative thread generation.

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Section: Related Workmentioning

confidence: 99%

Data Dependence Profiling for Speculative Optimizations

Chen

Lin

Dai

et al. 2004

Lecture Notes in Computer Science

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“…This is because statically identifying available parallelism, especially data parallelism, is hard. TEST [5] is a hardware profiler for decomposing java programs into speculative threads. It focuses on decompositions formed from loops.…”

Section: Related Workmentioning

confidence: 99%

“…MIN-CUT [14] identifies code regions that can run speculatively on CMPs by profiling the number of dependence edges that cross a program point. TEST [5] profiles the minimum dependence distance between speculative threads. In [23], dependence frequencies are profiled for critical regions and used as an estimation of available concurrency.…”

Section: Introductionmentioning

confidence: 99%

Alchemist: A Transparent Dependence Distance Profiling Infrastructure

Zhang

Navabi

Jagannathan

2009

2009 International Symposium on Code Generation and Optimization

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Abstract-Effectively migrating sequential applications to take advantage of parallelism available on multicore platforms is a well-recognized challenge. This paper addresses important aspects of this issue by proposing a novel profiling technique to automatically detect available concurrency in C programs. The profiler, called Alchemist, operates completely transparently to applications, and identifies constructs at various levels of granularity (e.g., loops, procedures, and conditional statements) as candidates for asynchronous execution. Various dependences including read-after-write (RAW), write-after-read (WAR), and write-after-write (WAW), are detected between a construct and its continuation, the execution following the completion of the construct. The time-ordered distance between program points forming a dependence gives a measure of the effectiveness of parallelizing that construct, as well as identifying the transformations necessary to facilitate such parallelization. Using the notion of post-dominance, our profiling algorithm builds an execution index tree at run-time. This tree is used to differentiate among multiple instances of the same static construct, and leads to improved accuracy in the computed profile, useful to better identify constructs that are amenable to parallelization. Performance results indicate that the profiles generated by Alchemist pinpoint strong candidates for parallelization, and can help significantly ease the burden of application migration to multicore environments.

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“…Chen et al extract the loop entries and exits by hands and monitor them using special instructions for annotations called sloop and eloop [31]. While their mechanism assumes hardware support, our pMarker method is performed on dynamic BT aiming for the runtime optimization or speculative execution.…”

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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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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TEST: a Tracer for Extracting Speculative Threads

Abstract: Thread-level speculation (TLS)

Cited by 25 publications

References 31 publications

Data Dependence Profiling for Speculative Optimizations

Data Dependence Profiling for Speculative Optimizations

Alchemist: A Transparent Dependence Distance Profiling Infrastructure

Identifying Program Loop Nesting Structures during Execution of Machine Code

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