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[Abstract] The usage of heterogeneous devices presents two main problems. One is their complex programming, a problem that grows when multiple devices are used. The second issue is that even if the codes for these devices can be portable on top of OpenCL, they lack performance portability, effectively requiring specialized implementations for each device to get good performance. In this paper we extend the Heterogeneous Programming Library (HPL), which improves the usability of heterogeneous systems on top of OpenCL, to better handle both issues. First, we provide HPL with mechanisms to support the implementation of any multi-device application that requires arbitrary patterns of communication between several devices and a host memory. In a second stage HPL is improved with an adaptive scheme to optimize communications between devices depending on the execution environment. An evaluation using benchmarks with very different nature shows that HPL reduces the SLOCs and programming effort of OpenCL applications by 27 and 43 %, respectively, while improving the performance of applications that exchange data between devices by 28 % on average.

[Abstract] While recognition of the advantages of heterogeneous computing is steadily growing, the issues of programmability and portability hinder its exploitation. The introduction of the OpenCL standard was a major step forward in that it provides code portability, but its interface is even more complex than that of other approaches. In this paper, we present the Heterogeneous Programming Library (HPL), which permits the development of heterogeneous applications addressing both portability and programmability while not sacrificing high performance. This is achieved by means of an embedded language and data types provided by the library with which generic computations to be run in heterogeneous devices can be expressed. A comparison in terms of programmability and performance with OpenCL shows that both approaches offer very similar performance, while outlining the programmability advantages of HPL.

Distributed memory multiprocessors are increasingly being used to provide high performance for advanced calculations with scientific applications. Distributed memory machines offer significant advantages over their shared memory counterparts in terms of cost and scalability, though it is widely accepted that they are difficult to program given the current status of software technology. Currently, distributed memory machines are programmed using a node language and a message passing library. This process is tedious and error prone because the user must perform the task of data distribution and communication for non-local data access. This thesis describes an advanced compiler that can generate efficient parallel programs when the source programming language naturally represents an application's parallelism. Fortran 90D/HPF described in this thesis is such a language. Using Fortran 90D/HPF, parallelism is represented with parallel constructs, such as array operations, where statements, forall statements, and intrinsic functions. The language provides directives for data distribution. Fortran 90D/HPF gives the programmer powerful tools to express a problem with natural data parallelism. To validate this hypothesis, a prototype of Fortran 90D/HPF was implemented. The compiler is organized around several major units: language parsing, partitioning data and computation, detecting communication and generating code. The compiler recognizes the presence of communication patterns in the computations in order to generate appropriate communication calls. Specifically, this involves a number of tests on the relationships among subscripts of various arrays in a statement. The compiler includes a specially designed algorithm to detect communications and to generate appropriate collective communication calls to execute array assignments and forall statements. The Fortran 90D/HPF compiler performs several types of communication and computation optimizations to improve the performanc