Tracing for web 3.0

Chang, Mason; Smith, Edwin; Reitmaier, Rick; Bebenita, Michael; Gal, Andreas; Wimmer, Christian; Eich, Brendan; Franz, Michael

doi:10.1145/1508293.1508304

Cited by 39 publications

(5 citation statements)

References 20 publications

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“…This system uses information collected at runtime to detect and optimize critical execution paths. Since the creation of this strategy, several JITsystems have employed it, such as TRACE MONKEY, 46 TAMARIN‐TRACE, 47 and HAPPY JIT 48…”

Section: Related Workmentioning

confidence: 99%

Smart selection of optimizations in dynamic compilers

Rosário

Silva

Camacho

et al. 2020

Concurrency and Computation

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Summary Dynamic compilers perform compilation and generation of target code during runtime, implying that the compilation time is added into the program runtime. Thus, to build a high‐performing dynamic compilation system, it is crucial to be able to generate high‐quality code and, at the same time, have a small compilation cost. In this article, we present an approach that uses machine learning to select sequences of optimization for dynamic compilation that considers both code quality and compilation overhead. Our approach starts by training a model, offline, with a knowledge bank of those sequences with low overhead and high‐quality code generation capability using a genetic heuristic. Then, this bank is used to guide the smart selection of optimizations sequences for the compilation of code fragments during the emulation of an application. We evaluate the proposed strategy in two LLVM‐based dynamic binary translators, namely OI‐DBT and HQEMU, and show that these two translators can achieve average speedups of 1.26x and 1.15x in MiBench and Spec Cpu benchmarks, respectively.

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Section: Related Workmentioning

confidence: 99%

Smart selection of optimizations in dynamic compilers

Rosário

Silva

Camacho

et al. 2020

Concurrency and Computation

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“…The syntax of each file is checked, using the W3C Validation Service 3 . Once the file has been validated it is converted into NTriples, using the Mindswap RDF converter tool 4 .…”

Section: Framework Implementationmentioning

confidence: 99%

“…This shift enabled the WWW to become a central part of our lives and has allowed user-driven applications, such as Facebook, YouTube and Flickr, to become key generators of information [2]. This transformation has redefined the browser as a vehicle for delivering richer media content, and interactivity, through a fusion of existing technologies, most notably Asynchronous JavaScript and XML (AJAX) [3]. Today, the 3rd generation (Web 3.0) is defined as the semantic web and has changed the web into a language that can be read and categorized by the system rather than humans [1].…”

Section: Introductionmentioning

confidence: 99%

Knowledge extraction using probabilistic reasoning: An artificial neural network approach

Dobbins

Fergus

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-The World Wide Web (WWW) has radically changed the way in which we access, generate and disseminate information. Its presence is felt daily and with more internetenabled devices being connected the web of knowledge is growing. We are now moving into era where the WWW is capable of 'understanding' the actual/intended meaning of our content. This is being achieved by creating links between distributed data sources using the Resource Description Framework (RDF). In order to find information in this web of interconnected sources, complex query languages are often employed, e.g. SPARQL. However, this approach is limited as exact query matches are often required. In order to overcome this challenge, this paper presents a probabilistic approach to searching RDF documents. The developed algorithm converts RDF data into a matrix of features and treats searching as a machine learning problem. Using a number of artificial neural network algorithms, a successfully developed prototype has been developed that demonstrates the applicability of the approach. The results illustrate that the Voted Perceptron classifier (VPC), perceptron linear classifier (PERLC) and random neural network classifier (RNNC) performed particularly well, with accuracies of 100%, 98% and 93% respectively.

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“…The technique that solves the branch prediction problem of switch-dispatch is called threaded code [Bell 1973] and has been previously explored by several researchers [Kogge 1982;Debaere and van Campenhout 1990]. The basic idea of threaded code is to "spread out" the instruction dispatch to each operation implementation, or i-op for short.…”

Section: Efficient Interpretationmentioning

confidence: 99%

“…In 1982, Kogge [1982 described the advantages of using threaded code and systematically analyzes its performance potential. In 1990, a book by Debaere and van Campenhout [1990] reported the state of the art for threaded code, including an in-depth discussion of all threaded code techniques known at the time, plus the effects of using a dedicated co-processor to "offload" the interpretative overhead from the critical path of the CPU. In 1993, Curley [1993aCurley [ , 1993b explained the subroutine threaded code for Forth.…”

Section: Threaded Codementioning

confidence: 99%

Efficient hosted interpreters on the JVM

Savrun-Yeniçeri

Zhang

et al. 2014

ACM Trans. Archit. Code Optim.

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Many guest languages are implemented using the Java Virtual Machine (JVM) as a host environment. There are two major implementation choices: custom compilers and so-called hosted interpreters. Custom compilers are complex to build but offer good performance. Hosted interpreters are comparatively simpler to implement but until now have suffered from poor performance.We studied the performance of hosted interpreters and identified common bottlenecks preventing their efficient execution. First, similar to interpreters written in C/C++, instruction dispatch is expensive on the JVM. Second, Java's semantics require expensive runtime exception checks that negatively affect array performance essential to interpreters.We present two optimizations targeting these bottlenecks and show that the performance of optimized interpreters increases dramatically: we report speedups by a factor of up to 2.45 over the Jython interpreter, 3.57 over the Rhino interpreter, and 2.52 over the JRuby interpreter, respectively. The resulting performance is comparable with that of custom compilers. Our optimizations are enabled by a few simple annotations that require only modest implementation effort; in return, performance increases substantially.

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Tracing for web 3.0

Cited by 39 publications

References 20 publications

Smart selection of optimizations in dynamic compilers

Smart selection of optimizations in dynamic compilers

Knowledge extraction using probabilistic reasoning: An artificial neural network approach

Efficient hosted interpreters on the JVM

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