Robert M. Farber

Topical perspective on massive threading and parallelism

Unquestionably computer architectures have undergone a recent and noteworthy paradigm shift that now delivers multi- and many-core systems with tens to many thousands of concurrent hardware processing elements per workstation or supercomputer node. GPGPU (General Purpose Graphics Processor Unit) technology in particular has attracted significant attention as new software development capabilities, namely CUDA (Compute Unified Device Architecture) and OpenCL™, have made it possible for students as well as small and large research organizations to achieve excellent speedup for many applications over more conventional computing architectures. The current scientific literature reflects this shift with numerous examples of GPGPU applications that have achieved one, two, and in some special cases, three-orders of magnitude increased computational performance through the use of massive threading to exploit parallelism. Multi-core architectures are also evolving quickly to exploit both massive-threading and massive-parallelism such as the 1.3 million threads Blue Waters supercomputer. The challenge confronting scientists in planning future experimental and theoretical research efforts--be they individual efforts with one computer or collaborative efforts proposing to use the largest supercomputers in the world is how to capitalize on these new massively threaded computational architectures--especially as not all computational problems will scale to massive parallelism. In particular, the costs associated with restructuring software (and potentially redesigning algorithms) to exploit the parallelism of these multi- and many-threaded machines must be considered along with application scalability and lifespan. This perspective is an overview of the current state of threading and parallelize with some insight into the future.

Social media and social reality

Social Media provide an exciting and novel view into social phenomena. The vast amounts of data that can be gathered from the Internet coupled with massively parallel supercomputers such as the Cray XMT open new vistas for research. Conclusions drawn from such analysis must recognize that social media are distinct from the underlying social reality. Rigorous validation is essential. This paper briefly presents results obtained from computational analysis of social media - utilizing both blog and twitter data. Validation of these results is discussed in the context of a framework of established methodologies from the social sciences. Finally, an outline for a set of supporting studies is proposed.

Thought leaders during crises in massive social networks

Making vast amounts of online social media data comprehensible to an analyst is a key question in operational analytics. Twitter and micro-blog conversations can easily be gathered from Internet services such as Spinn3r to create graphs representing the interactions between the entities in an online community that contains billions of vertices and tens of billions of edges. Graphs of this size can easily be represented in a modern laptop or workstation. The challenge lies in making them comprehensible. This paper focuses on methods to assemble social network graphs from online social media to reveal nodes that are ‘interesting’ in the context of operational analysis—meaning that the computational results can be interpreted by a human analyst wishing to answer some operational questions. Only metrics based on the structure of the graph are utilized, which avoid the challenges and costs involved in message content analysis. We further restrict ourselves to the use of metrics that are computational tractable on billion node graphs. The reported results demonstrate that nodes with a high impact or disproportionally large agency on the whole network (e.g., online community) can be found in a variety of online communities. Validation of the importance of these high-agency nodes by human and computational methods is discussed, and the efficacy of our approach by both quantitative methods and tests against the null hypothesis is reported.

Experimental comparison of emulated lock-free vs. fine-grain locked data structures on the Cray XMT

Three implementations of a concurrently-updateable linked list were compared, one that emulates a lock-free approach based on a compare-and-swap instruction, one that makes direct use of the Cray XMTs full-empty synchronization bits on every word of memory, and a third that uses the XMTs atomic int_fetch_add instruction. The relative performance of the three implementations was experimentally compared on a 512-processor XMT. The direct implementation approach performed up to twice as fast as the other two approaches under conditions of low contention, but the three implementations performed about the same when the amount of contention was high.

Sociolect-based community detection

“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 d iscuss their further extensions and potential applications.

Jargon and graph modularity on twitter

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 find communities, we combine large scale graph analytical techniques with known socio-linguistic methods. In this article we leverage both curated vocabularies and naive mathematical graph analyses to determine if community structure on Twitter corroborates with modern socio-linguistic theory. 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.