Parallel Computing of Graph-based Functions in ReRAM

Resistive Random Access Memory (ReRAM) is an emerging non-volatile memory technology. Besides its low power consumption and its high scalability, its inherent computation capabilities make ReRAM especially interesting for future computer architectures. Merging computations into the memory is a promising solution for overcoming the memory bottleneck. To perform computations in ReRAM, efficient synthesis strategies for Boolean functions have to be developed. In this article, we give a thorough presentation of how to employ parallel computing capabilities of ReRAM for the synthesis of functions given state-of-the-art graph-based representations AIGs or BDDs. Additionally, we introduce a new graph-based representation called m-And-Inverter Graph (m-AIGs), which allows us to fully exploit the computing capabilities of ReRAM. In the simulations, we show that our proposed approaches outperform state-of-the art synthesis strategies, and we show the superiority of m-AIGs over the standard AIG representation for ReRAM-based synthesis.

Fast nanopore sequencing data analysis with SLOW5

Nanopore sequencing depends on the FAST5 file format, which does not allow efficient parallel analysis. Here we introduce SLOW5, an alternative format engineered for efficient parallelization and acceleration of nanopore data analysis. Using the example of DNA methylation profiling of a human genome, analysis runtime is reduced from more than two weeks to approximately 10.5 h on a typical high-performance computer. SLOW5 is approximately 25% smaller than FAST5 and delivers consistent improvements on different computer architectures.

Classifying Co-Resident Computer Programs Using Information Revealed by Resource Contention

Modern computer architectures are complex, containing numerous components that can unintentionally reveal system operating properties. Defensive security professionals seek to minimize this kind of exposure while adversaries can leverage the data to attain an advantage. This paper presents a novel covert interrogator program technique using light-weight sensor programs to target integer, floating point, and memory units within a computer's architecture to collect data which can be used to match a running program to a known set of programs with up to 100\% accuracy under simultaneous multithreading conditions. This technique is applicable to a broad spectrum of architectural components, does not rely on specific vulnerabilities, nor requires elevated privileges. Furthermore, this research demonstrates the technique in a system with operating system containers intended to provide isolation guarantees which limit a user's ability to observe the activity of other users. In essence, this research exploits observable noise that is present whenever a program executes on a modern computer. This paper presents interrogator program design considerations, a machine learning approach to identify models with high classification accuracy, and measures the effectiveness of the approach under a variety of program execution scenarios.

A Deep Graph Network–Enhanced Sampling Approach to Efficiently Explore the Space of Reduced Representations of Proteins

The limits of molecular dynamics (MD) simulations of macromolecules are steadily pushed forward by the relentless development of computer architectures and algorithms. The consequent explosion in the number and extent of MD trajectories induces the need for automated methods to rationalize the raw data and make quantitative sense of them. Recently, an algorithmic approach was introduced by some of us to identify the subset of a protein’s atoms, or mapping, that enables the most informative description of the system. This method relies on the computation, for a given reduced representation, of the associated mapping entropy, that is, a measure of the information loss due to such simplification; albeit relatively straightforward, this calculation can be time-consuming. Here, we describe the implementation of a deep learning approach aimed at accelerating the calculation of the mapping entropy. We rely on Deep Graph Networks, which provide extreme flexibility in handling structured input data and whose predictions prove to be accurate and-remarkably efficient. The trained network produces a speedup factor as large as 105 with respect to the algorithmic computation of the mapping entropy, enabling the reconstruction of its landscape by means of the Wang–Landau sampling scheme. Applications of this method reach much further than this, as the proposed pipeline is easily transferable to the computation of arbitrary properties of a molecular structure.

Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage

Due to the increasing use of data analytics, information visualization is getting more and more important. However, as data get more complex, so does visualization, often leading to ad hoc and cumbersome solutions. A recent alternative is the use of the so-called knowledge-assisted visualization tools. In this paper, we present STMaps (Spatio-Temporal Maps), a multipurpose knowledge-assisted ontology-based visualization tool of spatio-temporal data. STMaps has been (originally) designed to show, by means of an interactive map, the content of the SILKNOW project, a European research project on silk heritage. It is entirely based on ontology support, as it gets the source data from an ontology and uses also another ontology to define how data should be visualized. STMaps provides some unique features. First, it is a multi-platform application. It can work embedded in an HTML page and can also work as a standalone application over several computer architectures. Second, it can be used for multiple purposes by just changing its configuration files and/or the ontologies on which it works. As STMaps relies on visualizing spatio-temporal data provided by an ontology, the tool could be used to visualize the results of any domain (in other cultural and non-cultural contexts), provided that its datasets contain spatio-temporal information. The visualization mechanisms can also be changed by changing the visualization ontology. Third, it provides different solutions to show spatio-temporal data, and also deals with uncertain and missing information. STMaps has been tested to browse silk-related objects, discovering some interesting relationships between different objects, showing the versatility and power of the different visualization tools proposed in this paper. To the best of our knowledge, this is also the first ontology-based visualization tool applied to silk-related heritage.