Integer Factorization with a Neuromorphic Sieve

The bound to factor large integers is dominated by the computational effort to discover numbers that are smooth, typically performed by sieving a polynomial sequence. On a von Neumann architecture, sieving has log-log amortized time complexity to check each value for smoothness. This work presents a neuromorphic sieve that achieves a constant time check for smoothness by exploiting two characteristic properties of neuromorphic architectures: constant time synaptic integration and massively parallel computation. The approach is validated by modifying msieve, one of the fastest publicly available integer factorization implementations, to use the IBM Neurosynaptic System (NS1e) as a coprocessor for the sieving stage.Comment: Fixed typos in equation for modular roots (Section II, par. 6; Section III, par. 2) and phase calculation (Section IV, par 2

Solving discrete logarithms on a 170-bit MNT curve by pairing reduction

Pairing based cryptography is in a dangerous position following the breakthroughs on discrete logarithms computations in finite fields of small characteristic. Remaining instances are built over finite fields of large characteristic and their security relies on the fact that the embedding field of the underlying curve is relatively large. How large is debatable. The aim of our work is to sustain the claim that the combination of degree 3 embedding and too small finite fields obviously does not provide enough security. As a computational example, we solve the DLP on a 170-bit MNT curve, by exploiting the pairing embedding to a 508-bit, degree-3 extension of the base field.Comment: to appear in the Lecture Notes in Computer Science (LNCS

Lunar Regolith Simulant Feed System for a Hydrogen Reduction Reactor System

One of the goals of In-Situ Resource Utilization (ISRU) on the moon is to produce oxygen from the lunar regolith which is present in the form of Ilmenite (FeTi03) and other compounds. A reliable and attainable method of extracting some of the oxygen from the lunar regolith is to use the hydrogen reduction process in a hot reactor to create water vapor which is then condensed and electrolyzed to obtain oxygen for use as a consumable. One challenge for a production system is to reliably acquire the regolith with an excavator hauler mobility platform and then introduce it into the reactor inlet tube which is raised from the surface and above the reactor itself. After the reaction, the hot regolith (-1000 C) must be expelled from the reactor for disposal by the excavator hauler mobility system. In addition, the reactor regolith inlet and outlet tubes must be sealed by valves during the reaction in order to allow collection of the water vapor by the chemical processing sub-system. These valves must be able to handle abrasive regolith passing through them as well as the heat conduction from the hot reactor. In 2008, NASA has designed and field tested a hydrogen reduction system called ROxygen in order to demonstrate the feasibility of extracting oxygen from lunar regolith. The field test was performed with volcanic ash known as Tephra on Mauna Kea volcano on the Big Island of Hawai'i. The tephra has similar properties to lunar regolith, so that it is regarded as a good simulant for the hydrogen reduction process. This paper will discuss the design, fabrication, operation, test results and lessons learned with the ROxygen regolith feed system as tested on Mauna Kea in November 2008

The SIOX architecture – coupling automatic monitoring and optimization of parallel I/O

Performance analysis and optimization of high-performance I/O systems is a daunting task. Mainly, this is due to the overwhelmingly complex interplay of the involved hardware and software layers. The Scalable I/O for Extreme Performance (SIOX) project provides a versatile environment for monitoring I/O activities and learning from this information. The goal of SIOX is to automatically suggest and apply performance optimizations, and to assist in locating and diagnosing performance problems. In this paper, we present the current status of SIOX. Our modular architecture covers instrumentation of POSIX, MPI and other high-level I/O libraries; the monitoring data is recorded asynchronously into a global database, and recorded traces can be visualized. Furthermore, we offer a set of primitive plug-ins with additional features to demonstrate the flexibility of our architecture: A surveyor plug-in to keep track of the observed spatial access patterns; an fadvise plug-in for injecting hints to achieve read-ahead for strided access patterns; and an optimizer plug-in which monitors the performance achieved with different MPI-IO hints, automatically supplying the best known hint-set when no hints were explicitly set. The presentation of the technical status is accompanied by a demonstration of some of these features on our 20 node cluster. In additional experiments, we analyze the overhead for concurrent access, for MPI-IO’s 4-levels of access, and for an instrumented climate application. While our prototype is not yet full-featured, it demonstrates the potential and feasibility of our approach