Julia: dynamism and performance reconciled by design

Bezanson, Jeff; Chen, Jiahao; Chung, Benjamin; Karpinski, Stefan; Shah, Viral B.; Vítek, Jan; Zoubritzky, Lionel

doi:10.1145/3276490

Cited by 71 publications

(66 citation statements)

References 31 publications

(29 reference statements)

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“…Julia is a high level programming language primarily intended for intense data management and scientific computing applications [52]. Although interpreted, it offers high performance [53] as it is based on the low level virtual machine (LLVM) infrastructure engine [54]. The capabilities and the respective performance of the various ML models of Julia are described in Reference [55] especially over massive graphics processing unit (GPU) arrays [56].…”

Section: Previous Workmentioning

confidence: 99%

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

et al. 2020

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Cognitive maps are high level representations of the key topological attributes of real or abstract spatial environments progressively built by a sequence of noisy observations. Currently such maps play a crucial role in cognitive sciences as it is believed this is how clusters of dedicated neurons at hippocampus construct internal representations. The latter include physical space and, perhaps more interestingly, abstract fields comprising of interconnected notions such as natural languages. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications among others to edge prediction, ontology alignment, and transfer learning. Fuzzy cognitive graphs have been proposed for representing maps with incomplete knowledge or errors caused by noisy or insufficient observations. The primary contribution of this article is the construction of cognitive map for the sixteen Myers-Briggs personality types with a tensor distance metric. The latter combines two categories of natural language attributes extracted from the namesake Kaggle dataset. To the best of our knowledge linguistic attributes are separated in categories. Moreover, a fuzzy variant of this map is also proposed where a certain personality may be assigned to up to two types with equal probability. The two maps were evaluated based on their topological properties, on their clustering quality, and on how well they fared against the dataset ground truth. The results indicate a superior performance of both maps with the fuzzy variant being better. Based on the findings recommendations are given for engineers and practitioners.

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Section: Previous Workmentioning

confidence: 99%

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

et al. 2020

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“…While primarily used for variables, environments can also be created explicitly, e.g., to be used as hashmaps. Libraries are loaded by the attach() function that adds an environment to the list 1 Pronounced like a trilled "r", the sound one makes upon realizing that arguments can modify the environment of the function they are given to. of environments.…”

Section: Environments In Rmentioning

confidence: 99%

“…The lookup of c in c (1,2) skips the argument c, since it is not a function. Instead, primitive c() is called to construct a vector.…”

Section: : G(f())mentioning

confidence: 99%

“…bad <-function() rm(list="c", envir=sys.frame(-1)) f(bad()) This time evaluation ends with an error as we try to add a vector and a function. Evaluation of c (1,2) succeeds and returns a vector. But, during the lookup of c for that call, R first encounters the argument c. In order to check if c is bound to a closure, it evaluates the promise, causing bad() to delete the argument from the environment.…”

Section: : G(f())mentioning

confidence: 99%

“…We run in four configurations: (1) default, (2) without promise inlining, (3) without scope resolution, and (4) with no optimizations. Figure 11 shows slowdowns relative to configuration (1). Disabling all optimizations slows down code by a range Figure 11.…”

Section: Effects On Optimizationsmentioning

confidence: 99%

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R melts brains: an IR for first-class environments and lazy effectful arguments

Flückiger

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Ječmen

et al. 2019

Proceedings of the 15th ACM SIGPLAN International Symposium on Dynamic Languages

Self Cite

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The R programming language combines a number of features considered hard to analyze and implement efficiently: dynamic typing, reflection, lazy evaluation, vectorized primitive types, first-class closures, and extensive use of native code. Additionally, variable scopes are reified at runtime as first-class environments. The combination of these features renders most static program analysis techniques impractical, and thus, compiler optimizations based on them ineffective. We present our work on PIR, an intermediate representation with explicit support for first-class environments and effectful lazy evaluation. We describe two dataflow analyses on PIR: the first enables reasoning about variables and their environments, and the second infers where arguments are evaluated. Leveraging their results, we show how to elide environment creation and inline functions.CCS Concepts • Software and its engineering → Compilers.

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Machine‐learning assisted scheduling optimization and its application in quantum chemical calculations

Chen

et al. 2023

J Comput Chem

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Easy and effective usage of computational resources is crucial for scientific calculations, both from the perspectives of timeliness and economic efficiency. This work proposes a bi-level optimization framework to optimize the computational sequences.Machine-learning (ML) assisted static load-balancing, and different dynamic load-balancing algorithms can be integrated. Consequently, the computational and scheduling engine of the PARAENGINE is developed to invoke optimized quantum chemical (QC) calculations. Illustrated benchmark calculations include highthroughput drug suit, solvent model, P38 protein, and SARS-CoV-2 systems. The results show that the usage rate of given computational resources for high throughput and large-scale fragmentation QC calculations can primarily profit, and faster accomplishing computational tasks can be expected when employing highperformance computing (HPC) clusters.

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Julia: dynamism and performance reconciled by design

Cited by 71 publications

References 31 publications

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

R melts brains: an IR for first-class environments and lazy effectful arguments

Machine‐learning assisted scheduling optimization and its application in quantum chemical calculations

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