Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS

Nowadays, with the rising number of sensors in sectors such as healthcare and industry, the problem of multivariate time series classification (MTSC) is getting increasingly relevant and is a prime target for machine and deep learning approaches. Their expanding adoption in real-world environments is causing a shift in focus from the pursuit of ever-higher prediction accuracy with complex models towards practical, deployable solutions that balance accuracy and parameters such as prediction speed. An MTSC model that has attracted attention recently is ROCKET, based on random convolutional kernels, both because of its very fast training process and its state-of-the-art accuracy. However, the large number of features it utilizes may be detrimental to inference time. Examining its theoretical background and limitations enables us to address potential drawbacks and present LightWaveS: a framework for accurate MTSC, which is fast both during training and inference. Specifically, utilizing wavelet scattering transformation and distributed feature selection, we manage to create a solution that employs just 2.5% of the ROCKET features, while achieving accuracy comparable to recent MTSC models. LightWaveS also scales well across multiple compute nodes and with the number of input channels during training. In addition, it can significantly reduce the input size and provide insight to an MTSC problem by keeping only the most useful channels. We present three versions of our algorithm and their results on distributed training time and scalability, accuracy, and inference speedup. We show that we achieve speedup ranging from 9x to 53x compared to ROCKET during inference on an edge device, on datasets with comparable accuracy.Comment: This work has been accepted as a short paper at DCOSS 202

Distributed MAP in the SpinJa Model Checker

Spin in Java (SpinJa) is an explicit state model checker for the Promela modelling language also used by the SPIN model checker. Designed to be extensible and reusable, the implementation of SpinJa follows a layered approach in which each new layer extends the functionality of the previous one. While SpinJa has preliminary support for shared-memory model checking, it did not yet support distributed-memory model checking. This tool paper presents a distributed implementation of a maximal accepting predecessors (MAP) search algorithm on top of SpinJa.Comment: In Proceedings PDMC 2011, arXiv:1111.006

Verifying Real-Time Systems using Explicit-time Description Methods

Timed model checking has been extensively researched in recent years. Many new formalisms with time extensions and tools based on them have been presented. On the other hand, Explicit-Time Description Methods aim to verify real-time systems with general untimed model checkers. Lamport presented an explicit-time description method using a clock-ticking process (Tick) to simulate the passage of time together with a group of global variables for time requirements. This paper proposes a new explicit-time description method with no reliance on global variables. Instead, it uses rendezvous synchronization steps between the Tick process and each system process to simulate time. This new method achieves better modularity and facilitates usage of more complex timing constraints. The two explicit-time description methods are implemented in DIVINE, a well-known distributed-memory model checker. Preliminary experiment results show that our new method, with better modularity, is comparable to Lamport's method with respect to time and memory efficiency

On the design of the Amoeba Configuration Manager

The program Amoeba Make, or Amake, is being designed to fulfil the need of a make-like configuration manager capable of exploiting the potentials of the Amoeba distributed operating system. The major design goal is to create a software configuration manager that is both easy to use and efficient. The specification and maintenance of a large configuration should be easy, and should be automated as much as possible. Furthermore, the build process should exploit Amoeba's capabilities and resources when creating or updating a target. In this paper we show how a smart file server can contribute to Amake's efficiency. We also show how a declarative configuration description allows Amake to take full advantage of parallelism and to determine the commands needed for building and maintaining targets. 1. INTRODUCTION The program Amake was designed to fulfil the need of a make-like configuration manager that tries to overcome make's inability to maintain large and complex systems in a convenien..

Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS

Nowadays, with the rising number of sensor signals in sectors such as healthcare and industry, the problem of multivariate time series classification (MTSC) is getting increasingly relevant and is a prime target for machine and deep learning approaches. Their expanding adoption in real-world environments is causing a shift in focus from the pursuit of everhigher prediction accuracy with complex models towards practical, deployable solutions that balance accuracy and parameters such as prediction speed. An MTSC model that has attracted attention recently is ROCKET, based on random convolutional kernels, both because of its very fast training process and its state-of-the-art accuracy. However, the large number of features it utilizes may be detrimental to inference time. Examining its theoretical background and limitations enables us to address potential drawbacks and present LightWaveS: a framework for accurate MTSC, which is fast both during training and inference. We show that LightWaveS achieves accuracy comparable to recent MTSC models and speedup ranging from 9x to 53x compared to ROCKET during inference on an edge device, on datasets with comparable accuracy

Fast Measurement of LogP Parameters for Message Passing Platforms

Abstract. Performance modeling is important for implementing efficient parallel applications and runtime systems. The LogP model captures the relevant aspects of message passing in distributed-memory architectures. In this paper we describe an efficient method that measures LogP parameters for a given message passing platform. Measurements are performed for messages of different sizes, as covered by the parameterized LogP model, a slight extension of LogP and LogGP. To minimize both intrusiveness and completion time of the measurement, we propose a procedure that sends as few messages as possible. An implementation of this procedure, called the MPI LogP benchmark, is available from our WWW site.