libVersioningCompiler: An easy-to-use library for dynamic generation and invocation of multiple code versions

Cherubin, Stefano; Agosta, Giovanni

doi:10.1016/j.softx.2018.03.006

Cited by 10 publications

(14 citation statements)

References 10 publications

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“…With this knowledge, we will be able to improve the policy and therefore the capabilities of the resource manager in taking decisions at run-time. Furthermore, we plan to extend the capabilities of the programming model to include dynamic recompilation of the kernels, through a partial dynamic compilation library supporting arbitrary C++ code [4].…”

Section: Discussionmentioning

confidence: 99%

Predictive Resource Management for Next-Generation High-Performance Computing Heterogeneous Platforms

Massari

Pupykina

Agosta

et al. 2019

Lecture Notes in Computer Science

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High-Performance Computing (HPC) is rapidly moving towards the adoption of nodes characterized by an heterogeneous set of processing resources. This has already shown benefits in terms of both performance and energy efficiency. On the other side, heterogeneous systems are challenging from the application development and the resource management perspective. In this work, we discuss some outcomes of the MANGO project, showing the results of the execution of real applications on a emulated deeply heterogeneous systems for HPC. Moreover, we assessed the achievements of a proposed resource allocation policy, aiming at identifying a priori the best resource allocation options for a starting application.

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Section: Discussionmentioning

confidence: 99%

Predictive Resource Management for Next-Generation High-Performance Computing Heterogeneous Platforms

Massari

Pupykina

Agosta

et al. 2019

Lecture Notes in Computer Science

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“…The subscriber registers a callback function to the given topic which is called every time a new message is received. To let LARA take advantage of this monitoring mechanism we have designed the Collector API, which allow the 1 aspectdef mARGOt_Aspect 2 / * Input: TargetFunctionCall * / 3 input targetCallName end 4 / * mARGOt configuration * / MargotCFun.LE, 0.03, 'error'); 12 13 / * optimization problem * / 14 var problem = targetBlock.newState('defaultState'); 15 problem.maximizeMetric('throughput'); 16 problem.subjectTo('my_error_goal'); 17 18 / * generate the information needed 19 for enhancing the application code * / 20 codegen = MargotCodeGen.fromConfig(config, $targetCallName); initialization of the Collector component associated with a specific topic that keeps an internal state of the remote sensor updated. This internal state can then be queried asynchronously by the Collector API to gather its value.…”

Section: Monitoringmentioning

confidence: 99%

The ANTAREX domain specific language for high performance computing

Silvano

Agosta

Bartolini

et al. 2019

Microprocessors and Microsystems

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The ANTAREX project relies on a Domain Specific Language (DSL) based on Aspect Oriented Programming (AOP) concepts to allow applications to enforce extra functional properties such as energy-efficiency and performance and to optimize Quality of Service (QoS) in an adaptive way. The DSL approach allows the definition of energyefficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. In this paper, we present an overview of the key outcome of the project, the ANTAREX DSL, and some of its capabilities through a number of examples, including how the DSL is applied in the context of the project use cases.

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“…This problem has a non-trivial impact on HPC infrastructures, as such systems usually aims at minimizing the access to persistent memory due to its intrinsic high-latency. Although recent proposals have been made to simplify the configuration of compiler settings [14], the limitation given by the memory access still persists. The JIT paradigm removes the problems of the abovementioned approaches, as it does not create any persistent object and it integrates the full compiler functionalities within the adaptive application.…”

Section: Continuous Optimization Via Dynamic Compilationmentioning

confidence: 99%

“…Autotuning frameworks can select one of a set of different versions of the same computational kernel to best fit the HPC system runtime conditions, such as system resource partitioning, as long as such versions are generated at compile time. Some frameworks are actually able to perform continuous optimization, generally through specific versions of a dynamic compiler [11; 12], or through cloud-based platforms [13], or by leveraging an external compiler through a dedicated API [14].…”

Section: Introductionmentioning

confidence: 99%

Continuous Program Optimization via Advanced Dynamic Compilation Techniques

Festa

Gervasoni

Cherubin

et al. 2019

Proceedings of the 10th and 8th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures

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In High Performance Computing, it is often useful to fine tune an application code via recompilation of specific computational intensive code fragments to leverage runtime knowledge. Traditional compilers rarely provide such capabilities, but solutions such as libVC exist to allow C/C++ code to employ dynamic compilation. We evaluate the impact of the introduction of Just-in-Time compilation in a framework supporting partial dynamic (re-)compilation of functions to provide continuous optimization in high performance environments. We show that Just-In-Time solutions can have comparable performance in terms of code quality with respect to the libVC alternatives, and it can provide smaller compilation overhead. We further demonstrate the strength of our approach against another interpreter-based dynamic evaluation solution from the state-of-the-art.

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libVersioningCompiler: An easy-to-use library for dynamic generation and invocation of multiple code versions

Cited by 10 publications

References 10 publications

Predictive Resource Management for Next-Generation High-Performance Computing Heterogeneous Platforms

Predictive Resource Management for Next-Generation High-Performance Computing Heterogeneous Platforms

The ANTAREX domain specific language for high performance computing

Continuous Program Optimization via Advanced Dynamic Compilation Techniques

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