High Performance Computing (HPC) has been a dominated technology used in seismic data processing at the petroleum industry. However, with the increasing data size and varieties, traditional HPC focusing on computation meets new challenges. Researchers are looking for new computing platforms with a balance of both performance and productivity, as well as featured with big data analytics capability. Apache Spark is a new big data analytics platform that supports more than map/reduce parallel execution mode with good scalability and fault tolerance. In this paper, we try to answer the question that if Apache Spark is scalable to process seismic data with its in-memory computation and data locality features. We use a few typical seismic data processing algorithms to study the performance and productivity. Our contributions include customized seismic data distributions in Spark, extraction of commonly used templates for seismic data processing algorithms, and performance analysis of several typical seismic processing algorithms.

The fast growing data volume poses significant challenges as well as opportunities to research and industry. Data scientists and domain experts demand high productive and high-performance data analytics platform, which will alleviate their work, tackle these challenges, and find more opportunities from data. The objective of our research is to build such a productive data analytics cloud platform by integrating a variety of data analytics tools and packages with a high-level workflow interface. Data scientists and domain experts are able to use the platform to process both big text and binary data with an interactive/batch/workflow interface to design their own applications in processing, analyzing, and visualizing data. In this paper, we present the current status of our big data analytics cloud at PVAMU as well as our research plan for future work. The usability and performance are main characteristics of this platform.

Stencil computations are core of wide range of scientific and engineering applications. A lot of efforts have been put into improving efficiency of stencil calculations on different platforms, but unfortunately it is not easy to reuse. In this paper we present a PAttern-Driven Stencil compiler-based tool and a simple tuning system to reuse those well optimized methods and codes. We also suggest extensions to OpenMP, depicting high-level data structures in order to facilitate recognition of various stencil computation patterns. The PADS allows programmers to rewrite kernel of stencils or reuse source-to-source translator outputs as optimized stencil template codes with related tuning parameters, In addition, PADS consists of a OpenMP to CUDA translator and code generator using optimized template codes. It also obtains architecture-specific parameters to tune stencils across different GPU platforms. To demonstrate our system flexibility and performance portability, we illustrate four different stencil computations, Laplacian operator with Jacobi iterative method, divergence operator, 3 dimension 25 point stencil and a 2D heat equation using ADI method with periodic boundary conditions. PADS succeeds in generating all these four stencil codes using different optimization strategies and delivers a promising performance improvement.

In this paper we discuss our initial experiences adapting OpenMP to enable it to serve as a programming model for high performance embedded systems. A high-level programming model such as OpenMP has the potential to increase programmer productivity, reducing the design/development costs and time to market for such systems. However, OpenMP needs to be extended if it is to meet the needs of embedded application developers, who require the ability to express multiple levels of parallelism, real-time and resource constraints, and to provide additional information in support of optimization. It must also be capable of supporting the mapping of different software tasks, or components, to the devices configured in a given architecture.

The advent of new parallel architectures has increased the need for parallel optimizing compilers to assist developers in creating efficient code. OpenUH is a state-of-the-art optimizing compiler, but it only performs a limited set of optimizations for OpenMP programs due to its conservative assumptions of shared memory programming. These limitations may prevent some OpenMP applications from being fully optimized to the extent of its sequential counterpart. This paper describes our design and implementation of a parallel data flow framework, consisting of a Parallel Control Flow Graph (PCFG) and a Parallel SSA (PSSA) representation in OpenUH, to model data flow for OpenMP programs. This framework enables the OpenUH compiler to perform all classical scalar optimizations for OpenMP programs, in addition to conducting OpenMP specific optimizations.