Comparing elementary cellular automata classifications with a convolutional neural network

in Proceedings of International Conference on Agents and Artificial Intelligence (ICAART) (2021, February 05)

Elementary cellular automata (ECA) are simple dynamic systems which display complex behaviour from simple local interactions. The complex behaviour is apparent in the two-dimensional temporal evolution of ... [more ▼]

Elementary cellular automata (ECA) are simple dynamic systems which display complex behaviour from simple local interactions. The complex behaviour is apparent in the two-dimensional temporal evolution of a cellular automata, which can be viewed as an image composed of black and white pixels. The visual patterns within these images inspired several ECA classifications, aimed at matching the automatas’ properties to observed patterns, visual or statistical. In this paper, we quantitatively compare 11 ECA classifications. In contrast to the a priori logic behind a classification, we propose an a posteriori evaluation of a classification. The evaluation employs a convolutional neural network, trained to classify each ECA to its assigned class in a classification. The prediction accuracy indicates how well the convolutional neural network is able to learn the underlying classification logic, and reflects how well this classification logic clusters patterns in the temporal evolution. Results show different prediction accuracy (yet all above 85%), three classifications are very well captured by our simple convolutional neural network (accuracy above 99%), although trained on a small extract from the temporal evolution, and with little observations (100 per ECA, evolving 513 cells). In addition, we explain an unreported ”pathological” behaviour in two ECAs. [less ▲]

Evolving a Deep Neural Network Training Time Estimator

in Communications in Computer and Information Science (2020, February)

We present a procedure for the design of a Deep Neural Net- work (DNN) that estimates the execution time for training a deep neural network per batch on GPU accelerators. The estimator is destined to be ... [more ▼]

We present a procedure for the design of a Deep Neural Net- work (DNN) that estimates the execution time for training a deep neural network per batch on GPU accelerators. The estimator is destined to be embedded in the scheduler of a shared GPU infrastructure, capable of providing estimated training times for a wide range of network architectures, when the user submits a training job. To this end, a very short and simple representation for a given DNN is chosen. In order to compensate for the limited degree of description of the basic network representation, a novel co-evolutionary approach is taken to fit the estimator. The training set for the estimator, i.e. DNNs, is evolved by an evolutionary algorithm that optimizes the accuracy of the estimator. In the process, the genetic algorithm evolves DNNs, generates Python-Keras programs and projects them onto the simple representation. The genetic operators are dynamic, they change with the estimator’s accuracy in order to balance accuracy with generalization. Results show that despite the low degree of information in the representation and the simple initial design for the predictor, co-evolving the training set performs better than near random generated population of DNNs. [less ▲]

Automatic Software Tuning of Parallel Programs for Energy-Aware Executions

in Proc. of 13th Intl. Conf. on Parallel Processing and Applied Mathematics (PPAM 2019) (2019, December)

For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to ... [more ▼]

For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to the type and characteristics of workload that the device is running. For this reason, this paper presents an automatic software tuning method for parallel program generation able to adapt and exploit the hardware features available on a target computing system such as an HPC facility or a cloud system in a better way than traditional compiler infrastructures. We propose a search based approach combining both exact methods and approximated heuristics evolving programs in order to find optimized configurations relying on an ever-increasing number of tunable knobs i.e., code transformation and execution options (such as the num- ber of OpenMP threads and/or the CPU frequency settings). The main objective is to outperform the configurations generated by traditional compiling infrastructures for selected KPIs i.e., performance, energy and power usage (for both for the CPU and DRAM), as well as the runtime. First experimental results tied to the local optimization phase of the proposed framework are encouraging, demonstrating between 8% and 41% improvement for all considered metrics on a reference benchmark- ing application (i.e., Linpack). This brings novel perspectives for the global optimization step currently under investigation within the presented framework, with the ambition to pave the way toward automatic tuning of energy-aware applications beyond the performance of the current state-of-the-art compiler infrastructures. [less ▲]

Energy-Performance Optimization for the Cloud

Doctoral thesis (2014)

It's Not a Bug, It'sa Feature: Wait-free Asynchronous Cellular Genetic Algorithm

in Wyrzykowski, Roman; Dongarra, Jack (Eds.) Parallel Processing and Applied Mathematics 10th International Conference, PPAM 2013 Warsaw, Poland, September 8–11, 2013 (2014)

Comparing the Performance and Power Usage of GPU and ARM Clusters for Map-Reduce

in Proc. of the 3rd Intl. Conf. on Cloud and Green Computing (CGC'13) (2013, October)

Solving very large instances of the scheduling of independent tasks problem on the GPU

in Journal of Parallel & Distributed Computing (2013), 73(1), 101-110

A two-phase heuristic for the energy-efficient scheduling of independent tasks on computational grids

in Cluster Computing (2012)

A Multi-Objective GRASP for Energy-aware Scheduling

in International Congress on Computer Science Research (2011, October 28)

Energy-efficient scheduling on milliclusters with performance constraints

in Green Computing and Communications (GreenCom), 2011 IEEE/ACM International Conference on (2011)

Today’s datacenters and large scale enterprise com- puting are power hungry. A lot of research effort is devoted in industry and academy to address this challenging issue. In this context, a new type of ... [more ▼]

Today’s datacenters and large scale enterprise com- puting are power hungry. A lot of research effort is devoted in industry and academy to address this challenging issue. In this context, a new type of enterprise computing platform is being investigated. This computing platform is composed of hundred of millicomputers, each requiring orders of magnitude less power. However, this approach brings challenges that must be met in order to compete with the current practice. This paper addresses two such critical challenges. First, it suggests how to decompose large applications into smaller tasks, better suited to millicomputers. Then, it casts the performance oriented and energy efficient problem into a soft real-time scheduling problem, for which several algorithms are then proposed and evaluated. Sensitivity analysis is used to provide insights into the model, and plan the evaluation of the scheduling algorithms. The contention found in multi-core millicomputing processors is also accounted for. [less ▲]