A collaborative dependence analysis framework

Nick, Palmen; Fix, Jordan; Stephen, Russell; Oh, Taewook; Thomas, B Jablin; Isaak, David

doi:10.1109/cgo.2017.7863736

Cited by 12 publications

(9 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Others [Porat et al 1996;Zendra et al 1997] do eliminate virtual tables entirely, however they require whole program visibility which prevents separate compilation and is not always feasible in practice. A number of recent works aim at making whole-program optimization and link-time optimization more effective [Doeraene and Schlatter 2016;Johnson et al 2017a;Sathyanathan et al 2017] or at improving call graph analyses [Johnson et al 2017b;Petrashko et al 2016;Tan et al 2017;Tip and Palsberg 2000] which can increase the scope and precision of devirtualization optimizations.…”

Section: Implementations Of Dynamic Dispatchmentioning

confidence: 99%

SAVI objects: sharing and virtuality incorporated

Hajj

Jablin

Milojičić

et al. 2017

Proc. ACM Program. Lang.

Self Cite

View full text Add to dashboard Cite

Direct sharing and storing of memory objects allows high-performance and low-overhead collaboration between parallel processes or application workflows with loosely coupled programs. However, sharing of objects is hindered by the inability to use subtype polymorphism which is common in object-oriented programming languages. That is because implementations of subtype polymorphism in modern compilers rely on using virtual tables stored at process-specific locations, which makes objects unusable in processes other than the creating process.In this paper, we present SAVI Objects, objects with Sharing and Virtuality Incorporated. SAVI Objects support subtype polymorphism but can still be shared across processes and stored in persistent data structures. We propose two different techniques to implement SAVI Objects and evaluate the tradeoffs between them. The first technique is virtual table duplication which adheres to the virtual-table-based implementation of subtype polymorphism, but duplicates virtual tables for shared objects to fixed memory addresses associated with each shared memory region. The second technique is hashing-based dynamic dispatch which re-implements subtype polymorphism using hashing-based look-ups to a global virtual table.Our results show that SAVI Objects enable direct sharing and storing of memory objects that use subtype polymorphism by adding modest overhead costs to object construction and dynamic dispatch time. SAVI Objects thus enable faster inter-process communication, improving the overall performance of production applications that share polymorphic objects.

show abstract

Section: Implementations Of Dynamic Dispatchmentioning

confidence: 99%

SAVI objects: sharing and virtuality incorporated

Hajj

Jablin

Milojičić

et al. 2017

Proc. ACM Program. Lang.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Perspective's memory analysis (CAF [20]) is composed of simple analysis algorithms that collaboratively resolve queries. The modularity of the analysis simplifies the addition of speculation awareness.…”

Section: Speculation-aware Memory Analyzermentioning

confidence: 99%

“…Perspective uses a state-of-the-art memory dependence analysis framework tailored for parallelization (CAF [20]), in which multiple simple analysis algorithms collaboratively attempt to disprove dependences and minimize the need for speculation. The utilized analysis algorithms reason about shape analysis, reachability, flow killing, induction variables, scalar evolution of pointers, and particular features of the LLVM IR and the C standard library.…”

Section: Static Analysismentioning

confidence: 99%

See 1 more Smart Citation

Perspective

Apostolakis

Chan

et al. 2020

Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Syste

Self Cite

View full text Add to dashboard Cite

The promise of automatic parallelization, freeing programmers from the error-prone and time-consuming process of making efficient use of parallel processing resources, remains unrealized. For decades, the imprecision of memory analysis limited the applicability of non-speculative automatic parallelization. The introduction of speculative automatic parallelization overcame these applicability limitations, but, even in the case of no misspeculation, these speculative techniques exhibit high communication and bookkeeping costs for validation and commit. This paper presents Perspective, a speculative-DOALL parallelization framework that maintains the applicability of speculative techniques while approaching the efficiency of non-speculative ones. Unlike current approaches which subsequently apply speculative techniques to overcome the imprecision of memory analysis, Perspective combines a novel speculation-aware memory analyzer, new efficient speculative privatization methods, and a planning phase to select a minimal-cost set of parallelizationenabling transforms. By reducing speculative parallelization overheads in ways not possible with prior parallelization systems, Perspective obtains higher overall program speedup (23.0× for 12 general-purpose C/C++ programs running on a 28-core shared-memory commodity machine) than Privateer (11.5×), the prior automatic DOALL parallelization system with the highest applicability.CCS Concepts • Software and its engineering → Compilers; Multithreading.

show abstract

“…In principle, we complement our PDG construction algorithm (stage 1 above) with profiling information similar to [24], i.e. for each of the statically detected may data dependences we refer to profiling information to resolve this may dependence as either must dependence or no dependence, based on whether or not a dependence was observed in the execution profile.…”

Section: Incorporating Profiling Informationmentioning

confidence: 99%

Generalized profile-guided iterator recognition

Manilov

Vasiladiotis

Franke

2018

Proceedings of the 27th International Conference on Compiler Construction

View full text Add to dashboard Cite

Iterators prescribe the traversal of data structures and determine loop termination, and many loop analyses and transformations require their exact identification. While recognition of iterators is a straight-forward task for affine loops, the situation is different for loops iterating over dynamic data structures or involving control flow dependent computations to determine the next data element. In this paper we propose a compiler analysis for recognizing loop iterators code for a wide class of loops. We initially develop a static analysis, which is then enhanced with profiling information to support speculative code optimizations. We have prototyped our analysis in the LLVM framework and demonstrate its capabilities using the SPEC CPU2006 benchmarks. Our approach is applicable to all loops and we show that we can recognize iterators in, on average, 88.1% of over 75,000 loops using static analysis alone, and up to 94.9% using additional profiling information. Existing techniques perform substantially worse, especially for C and C++ applications, and cover only 35-44% of the loops. Our analysis enables advanced loop optimizations such as decoupled software pipelining, commutativity analysis and source code rejuvenation for real-world applications, which escape analysis and transformation if loop iterators are not recognized accurately.

show abstract

A collaborative dependence analysis framework

Cited by 12 publications

References 25 publications

SAVI objects: sharing and virtuality incorporated

SAVI objects: sharing and virtuality incorporated

Perspective

Generalized profile-guided iterator recognition

Contact Info

Product

Resources

About