Parallel ruptures: Jews of Bessarabia and Transnistria between Romanian nationalism and Soviet communism, 1918-1940

“Parallel Ruptures: Jews of Bessarabia and Transnistria between Romanian Nationalism and Soviet Communism, 1918-1940,” explores the political and social debates that took place in Jewish communities in Romanian-held Bessarabia and the Moldovan Autonomous Soviet Socialist Republic during the interwar era. Both had been part of the Russian Pale of Settlement until its dissolution in 1917; they were then divided by the Romanian Army’s occupation of Bessarabia in 1918 with the establishment of a well-guarded border along the Dniester River between two newly-formed states, Greater Romania and the Soviet Union. At its core, the project focuses in comparative context on the traumatic and multi-faceted confrontation with these two modernizing states: exclusion, discrimination and growing violence in Bessarabia; destruction of religious tradition, agricultural resettlement, and socialist re-education and assimilation in Soviet Transnistria. It examines also the similarities in both states’ striving to create model subjects usable by the homeland, as well as commonalities within Jewish responses on both sides of the border. Contacts between Jews on either side of the border remained significant after 1918 despite the efforts of both states to curb them, thereby necessitating a transnational view in order to examine Jewish political and social life in borderland regions. The desire among Jewish secular leaders to mold their co-religionists into modern Jews reached across state borders and ideological divides and sought to manipulate respective governments to establish these goals, however unsuccessful in the final analysis. Finally, strained relations between Jews in peripheral borderlands with those at national/imperial cores, Moscow and Bucharest, sheds light on the complex circumstances surrounding the inclusion versus exclusion debates at the heart of all interwar European states and the complicated negotiations that took place within all minority communities that responded to state policies.

Design and implementation of a floating point unit for rigel, a massively parallel accelerator

Scientific applications rely heavily on floating point data types. Floating point operations are complex and require complicated hardware that is both area and power intensive. The emergence of massively parallel architectures like Rigel creates new challenges and poses new questions with respect to floating point support. The massively parallel aspect of Rigel places great emphasis on area efficient, low power designs. At the same time, Rigel is a general purpose accelerator and must provide high performance for a wide class of applications. This thesis presents an analysis of various floating point unit (FPU) components with respect to Rigel, and attempts to present a candidate design of an FPU that balances performance, area, and power and is suitable for massively parallel architectures like Rigel.

A parallel numerical solver using hierarchically tiled arrays

Solving linear systems is an important problem for scientific computing. Exploiting parallelism is essential for solving complex systems, and this traditionally involves writing parallel algorithms on top of a library such as MPI. The SPIKE family of algorithms is one well-known example of a parallel solver for linear systems. The Hierarchically Tiled Array data type extends traditional data-parallel array operations with explicit tiling and allows programmers to directly manipulate tiles. The tiles of the HTA data type map naturally to the block nature of many numeric computations, including the SPIKE family of algorithms. The higher level of abstraction of the HTA enables the same program to be portable across different platforms. Current implementations target both shared-memory and distributed-memory models. In this thesis we present a proof-of-concept for portable linear solvers. We implement two algorithms from the SPIKE family using the HTA library. We show that our implementations of SPIKE exploit the abstractions provided by the HTA to produce a compact, clean code that can run on both shared-memory and distributed-memory models without modification. We discuss how we map the algorithms to HTA programs as well as examine their performance. We compare the performance of our HTA codes to comparable codes written in MPI as well as current state-of-the-art linear algebra routines.