Abstract
Dynamic partitioning is a promising technique where computations are transparently moved from a General Purpose Processor (GPP) to a coprocessor during application execution. To be effective, the mapping of computations to the coprocessor needs to consider aggressive optimizations. One of the mapping optimizations is loop pipelining, a technique extensively studied and known to allow substantial performance improvements. This paper describes a technique for pipelining Megablocks, a type of runtime loop developed for dynamic partitioning. The technique transforms the body of Mega-blocks into an acyclic dataflow graph which can be fully pipe-lined and is based on the atomic execution of loop iterations. For a set of 9 benchmarks without memory operations, we generated pipelined hardware versions of the loops and esti-mate that the presented loop pipelining technique increases the average speedup of non-pipelined coprocessor accelerated designs from 1.6× to 2.2×. For a larger set of 61 benchmarks which include memory operations, we estimate through simulation a speedup increase from 2.5× to 5.6× with this technique.

Abstract
This paper describes MATISSE, a compiler able to translate a MATLAB subset to C targeting embedded systems. MATISSE uses LARA, an aspect-oriented programming language, to specify additional information and transformations to the input MATLAB code, for example, insertion of code for initialization of variables, and specification of types and shapes of variables. The compiler is being developed bearing in mind flexibility, multitarget and multitoolchain support, allowing for the generation of several implementations in C from the same reference code in MATLAB. In this paper, we also present a number of techniques being employed in MATLAB to C compilation, such as element-wise mapping operations, matrix views, weak types, and intrinsics. We validate these techniques using MATISSE and a set of representative benchmarks. More specifically, we evaluate the compiler with a set of 31 benchmarks using an embedded system board and a desktop computer. The results show speedups up to 1.8x by employing information provided by LARA aspects, when compared with C code generated without additional user information. When compared with the execution time of the original code running on MATLAB, the execution time of the generated C code achieved a geometric mean speedup of 13x. Copyright (c) 2016 John Wiley & Sons, Ltd.

Abstract
Hierarchical Data Format (HDF5) is a popular binary storage solution in high performance computing (HPC) and other scientific fields. It has bindings for many popular programming languages, including C++, which is widely used in the HPC field. Its C++ API requires mapping of the native C++ data types to types native to the HDF5 API. This task can be error prone, especially when working with complex data structures, which are usually stored using HDF5 compound data types. Due to the lack of a comprehensive reflection mechanism in C++, the mapping code for data manipulation has to be hand-written for each compound type separately. This approach is vulnerable to bugs and mistakes, which can be eliminated by using an automated code generation phase. In this paper we present an approach implemented in the LARA language and supported by the tool Clava, which allows us to automate the generation of the HDF5 data access code for complex data structures in C++.

Abstract
The main goal of the ANTAREX 1 project is to express by a Domain Specific Language (DSL) the application self-adaptivity and to runtime manage and autotune applications for green and heterogeneous High Performance Computing (HPC) systems up to the Exascale level. Key innovations of the project include the introduction of a separation of concerns between self-adaptivity strategies and application functionalities. The DSL approach will allow the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management.

Abstract
MATLAB to C translation is foreseen to raise the overall abstraction level when mapping computations to embedded systems (possibly consisting of software and hardware components), and thus for increasing productivity and for providing an automated modeldriven design-flow. This paper describes recent work developed in the context of MATISSE, a MATLAB to C compiler targeting embedded systems. We introduce several techniques to allow the efficient generation of C code, such as weak types, primitives and matrix views. We evaluate the compiler with a set of 9 publicly available benchmarks, targeting both embedded systems and a desktop system. We compare the execution time of the generated C code with the original code running on MATLAB, achieving a geometric mean speedup of 8.1 ×, and qualitatively compare our results with the performance of related approaches. The use of the new techniques allowed the compiler to achieve performance improvements of 46% on average.

Abstract
This paper describes MATISSE, a MATLAB to C compiler targeting embedded systems that is based on Strategic and Aspect-Oriented Programming concepts. MATISSE takes as input: (1) MATLAB code and (2) LARA aspects related to types and shapes, code insertion/ removal, and specialization based directives defining default variable values. In this paper we also illustrate the use of MATISSE in leveraging data types and shapes to generate customized C code suitable for high-level hardware synthesis tools. The preliminary experimental results presented here reveal the described approach to yield performance results for the resulting hardware and software references implementations that are comparable in terms of performance with hand-crafted solutions but derived automatically at a fraction of the cost.