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Asynchronous Execution of Python Code on Task-Based Runtime Systems
2018
2018 IEEE/ACM 4th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2)
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variables into a dependency tree executed by HPX, a general purpose, parallel, task-based runtime system written in C++. ...
Phylanx, is an asynchronous array processing toolkit which transforms Python and NumPy operations into code which can be executed in parallel on HPC resources by mapping Python and NumPy functions and ...
Phylanx tackles this issue by providing a framework that can execute arbitrary Python code in a distributed setting using an asynchronous many-task runtime system. ...
doi:10.1109/espm2.2018.00009
dblp:conf/sc/TohidWSDSKAWIHB18
fatcat:nv3ak23sgzafxhc524r3nsdzau
Extended Abstract: Productive Parallel Programming with Parsl
[article]
2022
arXiv
pre-print
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Developers can then link together functions via the exchange of data. Parsl establishes a dynamic dependency graph and sends tasks for execution on connected resources when dependencies are resolved. ...
Parsl relies on developers annotating Python functions-wrapping either Python or external applications-to indicate that these functions may be executed concurrently. ...
The graph is executed by translating the specification to code for a specific runtime system (e.g., in C++, Java, and .NET). ...
arXiv:2205.01527v1
fatcat:bli2ixefkrfabfpwzcmteyn3sa
torcpy: supporting task parallelism in Python
2020
Zenodo
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Task-based parallelism has been established as one of the main forms of code parallelization, where asynchronous tasks are launched and distributed across the processing units of a local machine, a cluster ...
In this work, we introduce torcpy, a platform-agnostic adaptive load balancing library that orchestrates the asynchronous execution of tasks, expressed as callables with arguments, on both shared and distributed ...
The core runtime is implemented in Java and allows for tasks that include MPI code but does not support direct injection of MPI SPMD code in the taskparallel Python code. ...
doi:10.5281/zenodo.3985059
fatcat:rhp5on4g5ngyhc5hpgtdb4b3be
Velociraptor
2014
Proceedings of the 23rd international conference on Parallel architectures and compilation - PACT '14
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Velociraptor provides an optimizing compiler toolkit for generating CPU and GPU code and also provides a smart runtime system to manage the GPU. ...
To demonstrate Velociraptor in action, we present two proof-of-concept case studies: a GPU extension for a JIT implementation of MATLAB language, and a JIT compiler for Python targeting CPUs and GPUs. ...
Their system required no annotations, and discovered sections of code suitable for execution on GPUs through profile-directed feedback. ...
doi:10.1145/2628071.2628097
dblp:conf/IEEEpact/GargH14
fatcat:b6qactysxnda3pe7enujb4vkkq
COMP Superscalar, an interoperable programming framework
2015
SoftwareX
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A runtime system is in charge of exploiting the inherent concurrency of the code, automatically detecting and enforcing the data dependencies between tasks and spawning these tasks to the available resources ...
For that purpose, it offers a simple programming model based on sequential development in which the user is mainly responsible for (i) identifying the functions to be executed as asynchronous parallel ...
When the sequential code is executed, the COMPSs runtime intercepts the methods invocations and replaces them with calls to the runtime that create new asynchronous tasks. ...
doi:10.1016/j.softx.2015.10.004
fatcat:wdihysbvvzgythxojl4uu7fbg4
The dog programming language
2013
Proceedings of the 26th annual ACM symposium on User interface software and technology - UIST '13
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in Dog in a few lines of code. ...
While these applications confer a number of benefits to their users, building them brings many challenges: manually managing state between asynchronous user actions, creating and maintaining separate code ...
Python, Ruby, Javascript). As a result, these languages are unable to take advantage of multicore CPUs.
Dog's Solution: Task-Based Concurrency Dog provides a concurrency primitive called tasks. ...
doi:10.1145/2501988.2502026
dblp:conf/uist/AhmadK13
fatcat:ew7fgxlmwjckpdbvk6b4yx72x4
torcpy: Supporting task parallelism in Python
2020
SoftwareX
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Task-based parallelism has been established as one of the main forms of code parallelization, where asynchronous tasks are launched and distributed across the processing units of a local machine, a cluster ...
In this work, we introduce torcpy, a platform-agnostic adaptive load balancing library that orchestrates the asynchronous execution of tasks, expressed as callables with arguments, on both shared and distributed ...
The core runtime is implemented in Java and allows for tasks that include MPI code but does not support direct injection of MPI SPMD code in the task-parallel Python code. ...
doi:10.1016/j.softx.2020.100517
fatcat:zghs427huje37po5iqvfjrma7e
NDL-v2.0: A new version of the numerical differentiation library for parallel architectures
2014
Computer Physics Communications
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On multicore clusters, parallelism is exploited by means of TORC, an MPI-based multi-threaded tasking library. ...
This allows sequential Python codes to exploit shared and distributed memory systems. ...
doi:10.1016/j.cpc.2014.04.002
fatcat:22p7akyem5cmhm5z23ktnxdhhu
FBBeam: An Erlang-based IEC 61499 Implementation
2019
2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
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Possible execution semantics are presented and compared to the Erlang execution model. An initial case study examines the scalability of a multi-tasking runtime environment. ...
This paper aims to investigate the benefits of reusing an existing soft real-time runtime system for the implementation of the IEC 61499. ...
The Erlang Runtime System (ERTS) is the virtual machine in which the compiled Erlang code is executed. ...
doi:10.1109/indin41052.2019.8972123
dblp:conf/indin/PrenzelP19
fatcat:ir7rech7xvhjvigai5uir3mwkm
Automatic Parallelization of Python Programs for Distributed Heterogeneous Computing
[article]
2022
arXiv
pre-print
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This paper introduces a novel approach to automatic ahead-of-time (AOT) parallelization and optimization of sequential Python programs for execution on distributed heterogeneous platforms. ...
Finally, the optimized output code is deployed using the Ray runtime for scheduling distributed tasks across multiple heterogeneous nodes in a cluster. ...
Shirako, Hayashi, Paul, Tumanov, Sarkar hybrid Python/C++ code generation, fine-grained NumPy-to-CuPy conversion, and profile-based CPU/GPU runtime selection. ...
arXiv:2203.06233v1
fatcat:4e7sa6j3szgfri5pajrgccuvuu
Toward Interlanguage Parallel Scripting for Distributed-Memory Scientific Computing
2015
2015 IEEE International Conference on Cluster Computing
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2021 pre-print arXiv:2107.02841v1 fatcat:tsv5q7njlvgcbkx54smmtgqphy
We present here a new approach to these problems in which the Swift scripting system is used to integrate high-level scripts written in Python, R, and Tcl, with native code developed in C, C++, and Fortran ...
However, deploying scripted applications on large-scale parallel computer systems such as the IBM Blue Gene/Q or Cray XE6 is a challenge because of issues including operating system limitations, interoperability ...
ACKNOWLEDGMENTS This material was based upon work supported by the U.S. Dept. of Energy, Office of Science, Office of Advanced Scientific Computing Research, under Contract DE-AC02-06CH11357. ...
doi:10.1109/cluster.2015.74
dblp:conf/cluster/WozniakAMKWF15
fatcat:kg2qojdvubbqhf75lhfzs6m2ae
Towards a Scalable and Distributed Infrastructure for Deep Learning Applications
[article]
2020
arXiv
pre-print
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parallelism and concurrency (HPX), leveraging fine-grained threading and an active messaging task-based runtime system. ...
Phylanx presents a productivity-oriented frontend where user Python code is translated to a futurized execution tree that can be executed efficiently on multiple nodes using the C++ standard library for ...
Acknowledgements The authors are grateful for the support of this work by the LSU Center for Computation & Technology and by the DTIC project: Phylanx Engine Enhancement and Visualizations Development ...
arXiv:2010.03012v1
fatcat:2hy7evtvdra2dotv35dvbhv7mu
Redesigning OP2 Compiler to Use HPX Runtime Asynchronous Techniques
[article]
2017
arXiv
pre-print
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One solution to address this challenge is the use of runtime methods. This strategy can be implemented by delaying certain amount of code analysis to be done at runtime. ...
These optimizations include asynchronous tasking, loop interleaving, dynamic chunk sizing, and data prefetching. ...
Acknowledgements We would like to thank Adrian Serio from Center for Computation and Technology at Louisiana State University for the invaluable and helpful comments and suggestions to improve the quality of ...
arXiv:1703.09264v1
fatcat:iwhar3uv5rfwxbz2mf7g7bkxlu
Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive HPC Workflows
[article]
2019
arXiv
pre-print
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We introduce the Balsam service to manage high-throughput task scheduling and execution on supercomputing systems. ...
workflows, in which tasks are created or killed at runtime. ...
Parsl [9] is a Python library that uses Swift/T as an execution backend for supercomputing environments, providing Python decorators to annotate existing codes for asynchronous, data-parallel execution ...
arXiv:1909.08704v1
fatcat:wqfpcf2vozbdzejvzqh3qrc75i
Parsl: Pervasive Parallel Programming in Python
[article]
2019
arXiv
pre-print
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These constructs allow Parsl to construct a dynamic dependency graph of components that it can then execute efficiently on one or many processors. ...
We show, via experiments on the Blue Waters supercomputer, that Parsl executors can allow Python scripts to execute components with as little as 5 ms of overhead, scale to more than 250 000 workers across ...
It relied on the Blue Waters sustainedpetascale computing project, which is supported by the National Science Foundation (OCI-0725070, ACI-1238993) and the State of Illinois. ...
arXiv:1905.02158v1
fatcat:okcga7i4vza6zmx5lyj63seone
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