FASTCUDA: Open Source FPGA Accelerator & Hardware-Software Codesign Toolset for CUDA Kernels

Using FPGAs as hardware accelerators that communicate with a central CPU is becoming a common practice in the embedded design world but there is no standard methodology and toolset to facilitate this path yet. On the other hand, languages such as CUDA and OpenCL provide standard development environments for Graphical Processing Unit (GPU) programming. FASTCUDA is a platform that provides the necessary software toolset, hardware architecture, and design methodology to efficiently adapt the CUDA approach into a new FPGA design flow. With FASTCUDA, the CUDA kernels of a CUDA-based application are partitioned into two groups with minimal user intervention: those that are compiled and executed in parallel software, and those that are synthesized and implemented in hardware. A modern low power FPGA can provide the processing power (via numerous embedded micro-CPUs) and the logic capacity for both the software and hardware implementations of the CUDA kernels. This paper describes the system requirements and the architectural decisions behind the FASTCUDA approach

Analysis of runtime re-configuration systems

In recent years Programmable Logic Devices (PLD) and in particular Field Programmable Gate Arrays (FPGAs) have seen a tremendous increase in sales and applications in the area of embedded systems. The main advantage of FPGAs is the flexibility that they offer a designer in reconfiguring the hardware. The flexibility achieved through re-configuration of FPGAs usually incurs an overhead of extra execution time, data memory and also power dissipation; FPGAs provide an ideal template for run-time reconfigurable (RTR) designs. Only recently have RTR enabling design tools that bypass the traditional synthesis and bitstream generation process for FPGAs become available, JBits is one of them. With run-time reconfiguration of FPGAs, we can perform partial reconfiguration, which allows reconfiguration of a part of an FPGA while the other part is executing some functional computation. The partial reconfiguration of a function can be performed earlier than the time when the function is really needed. Such configuration pre-fetch can hide the reconfiguration overhead more effectively; This thesis will implement a reconfigurable system and study the effect of runtime reconfiguration using VERILOG and a new Java based tool JBITS. This work will provide pointers to high level synthesis tools targeting runtime re-configuration

Application development process for GNAT, a SOC networked system

The market for smart devices was identified years ago, and yet commercial progress into this field has not made significant progress. The reason such devices are so painfully slow to market is that the gap between the technologically possible and the market capitalizable is too vast. In order for inventions to succeed commercially, they must bridge the gap to tomorrow\u27s technology with marketability today. This thesis demonstrates a design methodology that enables such commercial success for one variety of smart device, the Ambient Intelligence Node (AIN). Commercial Off-The Shelf (COTS) design tools allowing a Model-Driven Architecture (MDA) approach are combined via custom middleware to form an end-to-end design flow for rapid prototyping and commercialization. A walkthrough of this design methodology demonstrates its effectiveness in the creation of Global Network Academic Test (GNAT), a sample AIN. It is shown how designers are given the flexibility to incorporate IP Blocks available in the Global Economy to reduce Time-To-Market and cost. Finally, new kinds of products and solutions built on the higher levels of design abstraction permitted by MDA design methods are explored

Accelerating legacy applications with spatial computing devices

Heterogeneous computing is the major driving factor in designing new energy-efficient high-performance computing systems. Despite the broad adoption of GPUs and other specialized architectures, the interest in spatial architectures like field-programmable gate arrays (FPGAs) has grown. While combining high performance, low power consumption and high adaptability constitute an advantage, these devices still suffer from a weak software ecosystem, which forces application developers to use tools requiring deep knowledge of the underlying system, often leaving legacy code (e.g., Fortran applications) unsupported. By realizing this, we describe a methodology for porting Fortran (legacy) code on modern FPGA architectures, with the target of preserving performance/power ratios. Aimed as an experience report, we considered an industrial computational fluid dynamics application to demonstrate that our methodology produces synthesizable OpenCL codes targeting Intel Arria10 and Stratix10 devices. Although performance gain is not far beyond that of the original CPU code (we obtained a relative speedup of x 0.59 and x 0.63, respectively, for a single optimized main kernel, while only on the Stratix10 we achieved x 2.56 by replicating the main optimized kernel 4 times), our results are quite encouraging to drawn the path for further investigations. This paper also reports some major criticalities in porting Fortran code on FPGA architectures

Coarse-grained reconfigurable array architectures

Coarse-Grained Reconfigurable Array (CGRA) architectures accelerate the same inner loops that benefit from the high ILP support in VLIW architectures. By executing non-loop code on other cores, however, CGRAs can focus on such loops to execute them more efficiently. This chapter discusses the basic principles of CGRAs, and the wide range of design options available to a CGRA designer, covering a large number of existing CGRA designs. The impact of different options on flexibility, performance, and power-efficiency is discussed, as well as the need for compiler support. The ADRES CGRA design template is studied in more detail as a use case to illustrate the need for design space exploration, for compiler support and for the manual fine-tuning of source code

FPGA-based acceleration of the RMAP short read mapping tool

Bioinformatics is a quickly emerging field. Next generation sequencing technologies are producing data up to several gigabytes per day, making bioinformatics applications increasingly computationally intensive. In order to achieve greater speeds for processing this data, various techniques have been developed. These techniques involve parallelizing algorithms and/or spreading data across many computing nodes composed of devices such as Microprocessors, Graphics Processing Units (GPUs), and Field Programmable Gate Arrays (FPGAs). In this thesis, an FPGA is used to accelerate a bioinformatics application called RMAP, which is used for Short-Read Mapping. The most computationally intensive function in RMAP, the read mapping function, is implemented on the FPGA\u27s reconfigurable hardware fabric. This is a first step in a larger effort to develop a more optimal hardware/software co-design for RMAP. The Convey HC-1 Hybrid Computing System was used as the platform for development. The short-read mapping functionality of RMAP was implemented on one of the four Xilinx Virtex 5 FPGAs available in the HC-1 system. The RMAP 2.0 software was rewritten to separate the read mapping function to facilitate its porting over to hardware. The implemented design was evaluated by varying input parameters such as genome size and number of reads. In addition, the hardware design was analyzed to find potential bottlenecks. The implementation results showed a speedup of ~5x using datasets with varying number of reads and a fixed reference genome, and ~2x using datasets with varying genome size and a fixed number of reads, for the hardware-implemented short-read mapping function of RMAP

Methodology for complex dataflow application development

This thesis addresses problems inherent to the development of complex applications for reconfig- urable systems. Many projects fail to complete or take much longer than originally estimated by relying on traditional iterative software development processes typically used with conventional computers. Even though designer productivity can be increased by abstract programming and execution models, e.g., dataflow, development methodologies considering the specific properties of reconfigurable systems do not exist. The first contribution of this thesis is a design methodology to facilitate systematic develop- ment of complex applications using reconfigurable hardware in the context of High-Performance Computing (HPC). The proposed methodology is built upon a careful analysis of the original application, a software model of the intended hardware system, an analytical prediction of performance and on-chip area usage, and an iterative architectural refinement to resolve identi- fied bottlenecks before writing a single line of code targeting the reconfigurable hardware. It is successfully validated using two real applications and both achieve state-of-the-art performance. The second contribution extends this methodology to provide portability between devices in two steps. First, additional tool support for contemporary multi-die Field-Programmable Gate Arrays (FPGAs) is developed. An algorithm to automatically map logical memories to hetero- geneous physical memories with special attention to die boundaries is proposed. As a result, only the proposed algorithm managed to successfully place and route all designs used in the evaluation while the second-best algorithm failed on one third of all large applications. Second, best practices for performance portability between different FPGA devices are collected and evaluated on a financial use case, showing efficient resource usage on five different platforms. The third contribution applies the extended methodology to a real, highly demanding emerging application from the radiotherapy domain. A Monte-Carlo based simulation of dose accumu- lation in human tissue is accelerated using the proposed methodology to meet the real time requirements of adaptive radiotherapy.Open Acces