Computing plays an indispensable role in scientific research. Presently, researchers in science have different problems, needs, and beliefs about computation than professional programmers. In order to accelerate the progress of science, computer scientists must understand these problems, needs, and beliefs. To this end, this paper presents a survey of scientists from diverse disciplines, practicing computational science at a doctoral-granting university with very high research activity. The survey covers many things, among them, prevalent programming practices within this scientific community, the importance of computational power in different fields, use of tools to enhance performance and software productivity, computational resources leveraged, and prevalence of parallel computation. The results reveal several patterns that suggest interesting avenues to bridge the gap between scientific researchers and programming tools developers.
The performance benefits of GPU parallelism can be enormous, but unlocking this performance potential is challenging. The applicability and performance of GPU parallelizations is limited by the complexities of CPU-GPU communication. To address these communications problems, this paper presents the first fully automatic system for managing and optimizing CPU-GPU communcation. This system, called the CPU-GPU Communication Manager (CGCM), consists of a run-time library and a set of compiler transformations that work together to manage and optimize CPU-GPU communication without depending on the strength of static compile-time analyses or on programmer-supplied annotations. CGCM eases manual GPU parallelizations and improves the applicability and performance of automatic GPU parallelizations. For 24 programs, CGCM-enabled automatic GPU parallelization yields a whole program geomean speedup of 5.36x over the best sequential CPU-only execution.
GPUs are flexible parallel processors capable of accelerating real applications. To exploit them, programmers must ensure a consistent program state between the CPU and GPU memories by managing data. Manually managing data is tedious and error-prone. In prior work on automatic CPU-GPU data management, alias analysis quality limits performance, and type-inference quality limits applicability. This paper presents Dynamically Managed Data (DyManD), the first automatic system to manage complex and recursive data-structures without static analyses. By replacing static analyses with a dynamic run-time system, DyManD overcomes the performance limitations of alias analysis and enables management for complex and recursive data-structures. DyManD-enabled GPU parallelization matches the performance of prior work equipped with perfectly precise alias analysis for 27 programs and demonstrates improved applicability on programs not previously managed automatically.
Abstract. C++ Exceptions provide a useful way for dealing with abnormal program behavior, but often lead to irregular interprocedural control flow that complicates compiler optimizations and static analysis. In this paper, we present an interprocedural exception analysis and transformation framework for C++ that captures the control-flow induced by exceptions and transforms it into an exception-free program that is amenable for precise static analysis. Control-flow induced by exceptions is captured in a modular interprocedural exception control-flow graph (IECFG). The IECFG is further refined using a novel interprocedural dataflow analysis algorithm based on a compact representation for a set of types called the Signed-TypeSet domain. The results of the interprocedural analysis are used by a lowering transformation to generate an exception-free C++ program. The lowering transformations do not affect the precision and accuracy of any subsequent program analysis. Our framework handles all the features of synchronous C++ exception handling and all exception sub-typing rules from the C++0x standard. We demonstrate two applications of our framework: (a) automatic inference of exception specifications for C++ functions for documentation, and (b) checking the "no-throw" and "no-leak" exception-safety properties.
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