Mihaela Malita

The splicing mechanism and the Connex memory

Presents the main ideas concerning the implementation of a molecular mechanism in solid-state circuits: the splicing operation. The physical support for this operation is the Connex memory circuit (Stefan, 1986, 1995). Observing the similarities between a DNA computing-based mechanism and this new type of memory, we make a proposal to implement fine-grain computational parallelism on silicon. We promote this solution because a pure biological process is very hard to interface with machines in todays technologies. We make also evaluations of the complexity of our proposed machine.

Integral Parallel Architecture & Berkeley's Motifs

The Integral Parallel Architecture (IPA) developed and actually implemented by BrightScale is a low-power(133 GOPS/Watt) & low-area (8 GOPS/mm^2) one-chip solution to solve intense computational problems using data-parallel, time-parallel and speculative-parallel mechanisms. BrightScale technology is presented from the point of view of each of the 13 motifs proposed in The Berkeleys View. IPA emerges from Kleenes computational model of the partial recursive functions as the simplest parallel architecture, a good starting point for a true science of parallel computation. We briefly investigate how such an elementary parallel architecture performs, for the main computational motifs, in solving the problems of programmability, portability, flexibility, data movement between computational cells, and between cells and the main memory.

Local Alignments of DNA Sequences with the Connex Array

This paper presents a heuristic for finding close to optimal solutions to the local alignment problem of two DNA sequences, and more precisely to the gene prediction problem on the Connex Array circuit, a new hierarchical parallel in-memory device. Though not optimal, the solutions generated by our algorithm compare well with those generated by other algorithms in the public domain. When aligning a probe of N symbols to a target of M symbols, the algorithm has a theoretical time complexity of O(N log(N)), with a small constant of proportionality, and requires no preprocessing of the data. However, experimental results exhibit quasi-linear time complexity

Backus language for functional nano-devices

The emergent nano-technologies must be used to design functional nano-devices with complexity in the range of their size. The way to achieve this goal is to define parallel programmable engines. The concept of FP System, introduced by John Backus in 1978, is used to define a high level architectural environment: Backus-Connex Parallel Functional Programming System. The functional forms of FP Systems correspond perfect with the four types of parallelism derived for ConnexArray™ from the Kleenes computational model. The paper defines the BC programming environment, describes its implementation in Scheme and presents its use for developing real applications for functional nano-devices.

Functional Virtual Prototyping Environment for a Family of Map-Reduce Embedded Accelerators

This emergence of the heterogeneous computing is based mainly on various forms of parallel accelerators. We present a family of accelerators for embedded computation with a map-reduce architecture based on the partial recursive functions computation model introduced by Stephen Kleene. A three-level virtual prototyping environment is provided to support the development of embedded applications. The first level is written in a Lisp-like functional language. The second is a C-like environment which segregates the intense part of the computation from the complex part. The last one is a low level simulator able to provide support for advanced optimizations. The environment is designed for developing applications by tuning the architecture of a family of many-core machines which provide high performance per Watt and cm2. The energy efficiency of processors backing our architectural approach is in the range of 10 pJ/flop evaluated for the standard cell 28nm technology.

An Interdisciplinary Team Project: Psychology and Computer Science Students Create Online Cognitive Tasks.

We present our case study of an interdisciplinary team project for students taking either a psychology or computer science (CS) course. The project required psychology and CS students to combine their knowledge and skills to create an online cognitive task. Each interdisciplinary project team included two psychology students who conducted library research and drafted a design protocol for the cognitive task and a CS student who created the online version. The teams produced nine applets to test cognitive abilities such as memory and imagery. Our pre-post Interdisciplinary Team Project Survey indicated students gained significant experience working with each other by the end of the semester. Psychology students reported increases in areas such as summarizing data and delivering an oral presentation. CS students reported gains in computer programming. Leadership ratings moved upward for CS students, and psychology students reported greater experience conducting team projects by the end of the semester. CS students experienced some challenges with the interdisciplinary project regarding time management. We offer lessons learned and identify strategies for improving interdisciplinary team projects. An interdisciplinary team project prepares students for the type of work they will find in graduate school or workplace.