Co-array Fortran (CAF) and Unified Parallel C (UPC) are two emerging languages for single-program, multiple-data global address space programming. These languages boost programmer productivity by providing shared variables for inter-process communication instead of message passing. However, the performance of these emerging languages still has room for improvement. In this paper, we study the performance of variants of the NAS MG, CG, SP, and BT benchmarks on several modern architectures to identify challenges that must be met to deliver top performance. We compare CAF and UPC variants of these programs with the original Fortran+MPI code. Today, CAF and UPC programs deliver scalable performance on clusters only when written to use bulk communication. However, our experiments uncovered some significant performance bottlenecks of UPC codes on all platforms. We account for the root causes limiting UPC performance such as the synchronization model, the communication efficiency of strided data, and source-to-source translation issues. We show that they can be remedied with language extensions, new synchronization constructs, and, finally, adequate optimizations by the back-end C compilers.

Recently developed hyperspectral sensors provide much richer information than comparable multispectral sensors. However traditional methods that have been designed for multispectral data are not easily adaptable to hyperspectral data. One way to approach this problem is to perform dimension reduction as pre-processing, i.e. to apply a transformation that brings data from a high order dimension to a low order dimension. Wavelet spectral analysis of hyperspectral images has been recently proposed as a method for dimension reduction and, when tested on the classification of AVIRIS data, has shown promising results over the traditional principal component analysis (PCA) technique. We propose to extend and apply the wavelet analysis reduction method to the Atmospheric Infrared Sounder (AIRS) instrument data, designed to measure the Earth's atmospheric water vapor and temperature profiles on a global scale. With more than 2,000 channels, the AIRS infrared data represent a good candidate for dimension reduction, and especially wavelet reduction, due to its computational efficiency and the large data sizes involved.

Summary form only given. Parallel programming paradigms, over the past decade, have focused on how to harness the computational power of contemporary parallel machines. Ease of use and code development productivity, has been a secondary goal. Recently, however, there has been a growing interest in understanding the code development productivity issues and their implications for the overall time-to-solution. Unified Parallel C (UPC) is a recently developed language which has been gaining rising attention. UPC holds the promise of leveraging the ease of use of the shared memory model and the performance benefit of locality exploitation. The performance potential for UPC has been extensively studied in recent research efforts. The aim of this study, however, is to examine the impact of UPC on programmer productivity. We propose several productivity metrics and consider a wide array of high performance applications. Further, we compare UPC to the most widely used parallel programming paradigm, MPI. The results show that UPC compares favorably with MPI in programmers productivity.

UPC, or Unified Parallel C, is a parallel extension of ANSI C. UPC follows a distributed shared memory programming model aimed at leveraging the ease of programming of the shared memory paradigm, while enabling the exploitation of data locality. UPC incorporates constructs that allow placing data near the threads that manipulate them to minimize remote accesses. This paper gives an overview of the concepts and features of UPC and establishes, through extensive performance measurements of NPB workloads, the viability of the UPC programming language compared to the other popular paradigms. Further, through performance measurements we identify the challenges, the remaining steps and the priorities for UPC. It will be shown that with proper hand tuning and optimized collective operations libraries, UPC performance will be comparable to that of MPI. Furthermore, by incorporating such improvements into automatic compiler optimizations, UPC will compare quite favorably to message passing in ease of programming.