Today computer architectures are shifting from single core to manycores due to several reasons such as performance demands, power and heat limitations. However, shifting to manycores results in additional complexities, especially with regard to efficient development of applications. Hence there is a need to raise the abstraction level of development techniques for the manycores while exposing the inherent parallelism in the applications. One promising class of programming languages is dataflow languages and in this paper we evaluate and optimize the code generation for one such language, CAL. We have also developed a communication library to support the inter-core communication.The code generation can target multiple architectures, but the results presented in this paper is focused on Adapteva's many core architecture Epiphany.We use the two-dimensional inverse discrete cosine transform (2D-IDCT) as our benchmark and compare our code generation from CAL with a hand-written implementation developed in C. Several optimizations in the code generation as well as in the communication library are described, and we have observed that the most critical optimization is reducing the number of external memory accesses. Combining all optimizations we have been able to reduce the difference in execution time between auto-generated and hand-written implementations from a factor of 4.3x down to a factor of only 1.3x. ©2014 IEEE.

While parallel computer architectures have become mainstream, application development on them is still challenging. There is a need for new tools, languages and programming models. Additionally, there is a lack of knowledge about the performance of parallel approaches of basic but important operations, such as the QR decomposition of a matrix, on current commercial manycore architectures.

This paper evaluates a high level dataflow language (CAL), a source-to-source compiler (Cal2Many) and three QR decomposition algorithms (Givens Rotations, Householder and Gram-Schmidt). The algorithms are implemented both in CAL and hand-optimized C languages, executed on Adapteva's Epiphany manycore architecture and evaluated with respect to performance, scalability and development effort.

The performance of the CAL (generated C) implementations gets as good as 2\% slower than the hand-written versions. They require an average of 25\% fewer lines of source code without significantly increasing the binary size. Development effort is reduced and debugging is significantly simplified. The implementations executed on Epiphany cores outperform the GNU scientific library on the host ARM processor of the Parallella board by up to 30x. © 2016 Copyright held by the owner/author(s).

This paper proposes a novel method for performing division on floating-point numbers represented in IEEE-754 single-precision (binary32) format. The method is based on an inverter, implemented as a combination of Parabolic Synthesis and second-degree interpolation, followed by a multiplier. It is implemented with and without pipeline stages individually and synthesized while targeting a Xilinx Ultrascale FPGA.

The implementations show better resource usage and latency results when compared to other implementations based on different methods. In case of throughput, the proposed method outperforms most of the other works, however, some Altera FPGAs achieve higher clock rate due to the differences in the DSP slice multiplier design.

Due to the small size, low latency and high throughput, the presented floating-point division unit is suitable for high performance embedded systems and can be integrated into accelerators or be used as a stand-alone accelerator.

Performance and power requirements has pushed computer architectures from single core to manycores. These requirements now continue pushing the manycores with identical cores (homogeneous) to manycores with specialized cores (heterogeneous). However designing heterogeneous manycores is a challenging task due to the complexity of the architectures. We propose an approach for designing domain specific heterogeneous manycore architectures based on building blocks. These blocks are defined as the common computations of the applications within a domain. The objective is to generate heterogeneous architectures by integrating many of these blocks to many simple cores and connect the cores with a networkon-chip. The proposed approach aims to ease the design of heterogeneous manycore architectures and facilitate usage of dark silicon concept. As a case study, we develop an accelerator based on several building blocks, integrate it to a RISC core and synthesize on a Xilinx Ultrascale FPGA. The results show that executing a hot-spot of an application on an accelerator based on building blocks increases the performance by 15x, with room for further improvement. The area usage increases as well, however there are potential optimizations to reduce the area usage. © 2018 by the authors