Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

We propose Paraiso, a domain specific language embedded in functional programming language Haskell, for automated tuning of explicit solvers of partial differential equations (PDEs) on GPUs as well as multicore CPUs. In Paraiso, one can describe PDE solving algorithms succinctly using tensor equations notation. Hydrodynamic properties, interpolation methods and other building blocks are described in abstract, modular, re-usable and combinable forms, which lets us generate versatile solvers from little set of Paraiso source codes. We demonstrate Paraiso by implementing a compressive hydrodynamics solver. A single source code less than 500 lines can be used to generate solvers of arbitrary dimensions, for both multicore CPUs and GPUs. We demonstrate both manual annotation based tuning and evolutionary computing based automated tuning of the program.Comment: 52 pages, 14 figures, accepted for publications in Computational Science and Discover

Reverse engineering a gene network using an asynchronous parallel evolution strategy.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: The use of reverse engineering methods to infer gene regulatory networks by fitting mathematical models to gene expression data is becoming increasingly popular and successful. However, increasing model complexity means that more powerful global optimisation techniques are required for model fitting. The parallel Lam Simulated Annealing (pLSA) algorithm has been used in such approaches, but recent research has shown that island Evolutionary Strategies can produce faster, more reliable results. However, no parallel island Evolutionary Strategy (piES) has yet been demonstrated to be effective for this task. RESULTS: Here, we present synchronous and asynchronous versions of the piES algorithm, and apply them to a real reverse engineering problem: inferring parameters in the gap gene network. We find that the asynchronous piES exhibits very little communication overhead, and shows significant speed-up for up to 50 nodes: the piES running on 50 nodes is nearly 10 times faster than the best serial algorithm. We compare the asynchronous piES to pLSA on the same test problem, measuring the time required to reach particular levels of residual error, and show that it shows much faster convergence than pLSA across all optimisation conditions tested. CONCLUSIONS: Our results demonstrate that the piES is consistently faster and more reliable than the pLSA algorithm on this problem, and scales better with increasing numbers of nodes. In addition, the piES is especially well suited to further improvements and adaptations: Firstly, the algorithm's fast initial descent speed and high reliability make it a good candidate for being used as part of a global/local search hybrid algorithm. Secondly, it has the potential to be used as part of a hierarchical evolutionary algorithm, which takes advantage of modern multi-core computing architectures

Enhanced parallel Differential Evolution algorithm for problems in computational systems biology

[Abstract] Many key problems in computational systems biology and bioinformatics can be formulated and solved using a global optimization framework. The complexity of the underlying mathematical models require the use of efficient solvers in order to obtain satisfactory results in reasonable computation times. Metaheuristics are gaining recognition in this context, with Differential Evolution (DE) as one of the most popular methods. However, for most realistic applications, like those considering parameter estimation in dynamic models, DE still requires excessive computation times. Here we consider this latter class of problems and present several enhancements to DE based on the introduction of additional algorithmic steps and the exploitation of parallelism. In particular, we propose an asynchronous parallel implementation of DE which has been extended with improved heuristics to exploit the specific structure of parameter estimation problems in computational systems biology. The proposed method is evaluated with different types of benchmarks problems: (i) black-box global optimization problems and (ii) calibration of non-linear dynamic models of biological systems, obtaining excellent results both in terms of quality of the solution and regarding speedup and scalability.Ministerio de Economía y Competitividad; DPI2011-28112-C04-03Consejo Superior de Investigaciones Científicas; PIE-201170E018Ministerio de Ciencia e Innovación; TIN2013-42148-PGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013/05

Towards cloud-based parallel metaheuristics: A case study in computational biology with Differential Evolution and Spark

[Abstract] Many key problems in science and engineering can be formulated and solved using global optimization techniques. In the particular case of computational biology, the development of dynamic (kinetic) models is one of the current key issues. In this context, the problem of parameter estimation (model calibration) remains as a very challenging task. The complexity of the underlying models requires the use of efficient solvers to achieve adequate results in reasonable computation times. Metaheuristics have been the focus of great consideration as an efficient way of solving hard global optimization problems. Even so, in most realistic applications, metaheuristics require a very large computation time to obtain an acceptable result. Therefore, several parallel schemes have been proposed, most of them focused on traditional parallel programming interfaces and infrastructures. However, with the emergence of cloud computing, new programming models have been proposed to deal with large-scale data processing on clouds. In this paper we explore the applicability of these new models for global optimization problems using as a case study a set of challenging parameter estimation problems in systems biology. We have developed, using Spark, an island-based parallel version of Differential Evolution. Differential Evolution is a simple population-based metaheuristic that, at the same time, is very popular for being very efficient in real function global optimization. Several experiments were conducted both on a cluster and on the Microsoft Azure public cloud to evaluate the speedup and efficiency of the proposal, concluding that the Spark implementation achieves not only competitive speedup against the serial implementation, but also good scalability when the number of nodes grows. The results can be useful for those interested in using parallel metaheuristics for global optimization problems benefiting from the potential of new cloud programming models.Ministerio de Economía y Competitividad and FEDER; through the Project SYNBIOFACTORY; DPI2014-55276-C5-2-RMinisterio de Economía y Competitividad and FEDER; TIN2013-42148-PMinisterio de Economía y Competitividad and FEDER; TIN2016-75845-PXunta de Galicia; R2014/04

A flexible and efficient multi-purpose optimization library in python

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (Gpol): A flexible and efficient multi-purpose optimization library in python. Applied Sciences (Switzerland), 11(11), 1-34. [4774]. https://doi.org/10.3390/app11114774Several interesting libraries for optimization have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. In this paper, we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimization and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction).publishersversionpublishe

SGXTuner: Performance Enhancement of Intel SGX Applications via Stochastic Optimization

Intel SGX has started to be widely adopted. Cloud providers (Microsoft Azure, IBM Cloud, Alibaba Cloud) are offering new solutions, implementing data-in-use protection via SGX. A major challenge faced by both academia and industry is providing transparent SGX support to legacy applications. The approach with the highest consensus is linking the target software with SGX-extended libc libraries. Unfortunately, the increased security entails a dramatic performance penalty, which is mainly due to the intrinsic overhead of context switches, and the limited size of protected memory. Performance optimization is non-trivial since it depends on key parameters whose manual tuning is a very long process. We present the architecture of an automated tool, called SGXTuner, which is able to find the best setting of SGX-extended libc library parameters, by iteratively adjusting such parameters based on continuous monitoring of performance data. The tool is to a large extent algorithm agnostic. We decided to base the current implementation on a particular type of stochastic optimization algorithm, specifically Simulated Annealing. A massive experimental campaign was conducted on a relevant case study. Three client-server applications Memcached, Redis, and Apache were compiled with SCONE's sgx-musl and tuned for best performance. Results demonstrate the effectiveness of SGXTuner