Engineering topics
Publications and source records attributed to Farber, Robert M..
A portion of a chapter for the High Performance Parallelism Pearls 2 book. By using the NWPerf and CView software developed and deployed at PNNL administrators and users can visually see the performance, and power used by jobs running on a super computer in near-time.
The language of conversation is just as dependent upon word choice as it is on who is taking part. Twitter provides an excellent test-bed in which to conduct experiments not only on language usage but on who is using what language with whom. To this end, we combine large scale graph analytical techniques with known socio-linguistic methods. In this article we leverage both expert curated vocabularies and naive mathematical graph analyses to determine if network behavior on Twitter corroborates with the current understanding of language usage. The results reported indicate that, based on networks constructed from user to user communication and communities identified using the Clauset- Newman greedy modularity algorithm we find that more prolific users of these curated vocabularies are concentrated in distinct network communities.
"Sociolects" are specialized vocabularies used by social subgroups defined by common interests or origins. We applied methods to retrieve large quantities of Twitter data based on expert-identified sociolects and then applied and developed network-analysis methods to relate sociolect use to network (sub-) structure. We show that novel methods including consideration of node populations, as well as edge counts, provide substantially enhanced performance compared to standard assortativity. We explain these methods, show their utility in analyzing large corpora of social media data, and discuss their further extensions and potential applications.
Neural networks are a very useful tool for analyzing and modeling complex real world systems. Applying neural network simulations to real world problems generally involves large amounts of data and massive amounts of computation. To efficiently handle the computational requirements of large problems, we have implemented at Los Alamos a highly efficient neural network compiler for serial computers, vector computers, vector parallel computers, and fine grain SIMD computers such as the CM-2 connection machine. This paper describes the mapping used by the compiler to implement feed-forward backpropagation neural networks for a SIMD (Single Instruction Multiple Data) architecture parallel computer. Thinking Machines Corporation has benchmarked our code at 1.3 billion interconnects per second (approximately 3 gigaflops) on a 64,000 processor CM-2 connection machine (Singer 1990). This mapping is applicable to other SIMD computers and can be implemented on MIMD computers such as the CM-5 connection machine. Our mapping has virtually no communications overhead with the exception of the communications required for a global summation across the processors (which has a sub-linear runtime growth on the order of O(log(number of processors)). We can efficiently model very large neural networks which have many neurons and interconnects and our mapping can extend to arbitrarily large networks (within memory limitations) by merging the memory space of separate processors with fast adjacent processor interprocessor communications. This paper will consider the simulation of only feed forward neural network although this method is extendable to recurrent networks.