An automated OpenCL FPGA compilation framework targeting a configurable, VLIW chip multiprocessor

Modern system-on-chips augment their baseline CPU with coprocessors and accelerators to increase overall computational capacity and power efficiency, and thus have evolved into heterogeneous systems. Several languages have been developed to enable this paradigm shift, including CUDA and OpenCL. This thesis discusses a unified compilation environment to enable heterogeneous system design through the use of OpenCL and a customised VLIW chip multiprocessor (CMP) architecture, known as the LE1. An LLVM compilation framework was researched and a prototype developed to enable the execution of OpenCL applications on the LE1 CPU. The framework fully automates the compilation flow and supports work-item coalescing to better utilise the CPU cores and alleviate the effects of thread divergence. This thesis discusses in detail both the software stack and target hardware architecture and evaluates the scalability of the proposed framework on a highly precise cycle-accurate simulator. This is achieved through the execution of 12 benchmarks across 240 different machine configurations, as well as further results utilising an incomplete development branch of the compiler. It is shown that the problems generally scale well with the LE1 architecture, up to eight cores, when the memory system becomes a serious bottleneck. Results demonstrate superlinear performance on certain benchmarks (x9 for the bitonic sort benchmark with 8 dual-issue cores) with further improvements from compiler optimisations (x14 for bitonic with the same configuration

マルチレベル並列化とアプリケーション指向データレイアウトを用いるハードウェアアクセラレータの設計と実装

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 稲葉 雅幸, 東京大学教授 須田 礼仁, 東京大学教授 五十嵐 健夫, 東京大学教授 山西 健司, 東京大学准教授 稲葉 真理, 東京大学講師 中山 英樹University of Tokyo(東京大学

Text Preprocessing in Programmable Logic

There is a tremendous amount of information being generated and stored every year, and its growth rate is exponential. From 2008 to 2009, the growth rate was estimated to be 62%. In 2010, the amount of generated information is expected to grow by 50% to 1.2 Zettabytes, and by 2020 this rate is expected to grow to 35 Zettabytes. By preprocessing text in programmable logic, high data processing rates could be achieved with greater power efficiency than with an equivalent software solution, leading to a smaller carbon footprint. This thesis presents an overview of the fields of Information Retrieval and Natural Language Processing, and the design and implementation of four text preprocessing modules in programmable logic: UTF–8 decoding, stop–word filtering, and stemming with both Lovins’ and Porter’s techniques. These extensively pipelined circuits were implemented in a high performance FPGA and found to sustain maximum operational frequencies of 704 MHz, data throughputs in excess of 5 Gbps and efficiencies in the range of 4.332 – 6.765 mW/Gbps and 34.66 – 108.2 uW/MHz. These circuits can be incorporated into larger systems, such as document classifiers and information extraction engines

Parallelization of dynamic programming recurrences in computational biology

The rapid growth of biosequence databases over the last decade has led to a performance bottleneck in the applications analyzing them. In particular, over the last five years DNA sequencing capacity of next-generation sequencers has been doubling every six months as costs have plummeted. The data produced by these sequencers is overwhelming traditional compute systems. We believe that in the future compute performance, not sequencing, will become the bottleneck in advancing genome science. In this work, we investigate novel computing platforms to accelerate dynamic programming algorithms, which are popular in bioinformatics workloads. We study algorithm-specific hardware architectures that exploit fine-grained parallelism in dynamic programming kernels using field-programmable gate arrays: FPGAs). We advocate a high-level synthesis approach, using the recurrence equation abstraction to represent dynamic programming and polyhedral analysis to exploit parallelism. We suggest a novel technique within the polyhedral model to optimize for throughput by pipelining independent computations on an array. This design technique improves on the state of the art, which builds latency-optimal arrays. We also suggest a method to dynamically switch between a family of designs using FPGA reconfiguration to achieve a significant performance boost. We have used polyhedral methods to parallelize the Nussinov RNA folding algorithm to build a family of accelerators that can trade resources for parallelism and are between 15-130x faster than a modern dual core CPU implementation. A Zuker RNA folding accelerator we built on a single workstation with four Xilinx Virtex 4 FPGAs outperforms 198 3 GHz Intel Core 2 Duo processors. Furthermore, our design running on a single FPGA is an order of magnitude faster than competing implementations on similar-generation FPGAs and graphics processors. Our work is a step toward the goal of automated synthesis of hardware accelerators for dynamic programming algorithms

Advances in Stereo Vision

Stereopsis is a vision process whose geometrical foundation has been known for a long time, ever since the experiments by Wheatstone, in the 19th century. Nevertheless, its inner workings in biological organisms, as well as its emulation by computer systems, have proven elusive, and stereo vision remains a very active and challenging area of research nowadays. In this volume we have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints

Description and Specialization of Coarse-grained Reconfigurable Architectures

The functionality of electronic embedded systems, such as mobile phones and digital cameras, becomes more complex at each product generation. This increasing complexity implies great challenges at the design phase of these devices, as designers have to deal with high performance and low energy requirements at a low production budget. In the last years, coarse-grained, dynamically reconfigurable computer systems have increasingly gain in importance as an alternative to cope with these challenges because they provide an optimal trade-off between flexibility-after-production and performance. Like generic purpose processors, coarse-grained reconfigurable systems can be quickly reprogrammed to perform new tasks, but they keep their performance and energy consumption near to ASIC standards. The design of coarse-grained reconfigurable processors is the main theme in this work. In the first part of this dissertation, I present a new architecture description language that was designed for the description of coarse-grained, reconfigurable systems. This language allows an efficient specification of processor arrays and the description of scalable interconnection networks. The second part of this dissertation investigates the specialization of coarse-grained reconfigurable processors towards an application domain by using custom instruction sets. This work presents methods, techniques, and tools to recognize and extract clusters of operations from a set of application. These clusters serve as patterns for the design of an optimal custom instruction set. Experiments and results are presented, which analyze and assess the impact of custom instructions on coarse-grained processor arrays.Die Funktionalität eingebetteter Systeme wie Mobiltelefone und digitale Foto-Kameras wird zunehmend umfangreicher und bürdet dem Entwurf dieser Geräte hohe Herausforderungen auf, wie z.B. hohe Ausführungsgeschwindigkeit, niedrige Herstellungskosten und geringeren Energieverbrauch. Um diese Herausforderungen zu bewältigen, gewinnen grobgranulare dynamische rekonfigurierbare Rechnersysteme schnell an Bedeutung, denn sie bieten einen optimalen trade-off zwischen Flexibilität nach der Herstellung und Performanz. Wie allgemeine Prozessoren, können grobgranulare rekonfigurierbare Systeme während der Ausführungszeit schnell umprogrammiert werden, um neue Funktionalitäten auszuführen, behalten aber immer noch eine ASIC-ähnliche Performanz und Verlustleistungsverbrauch. Der Entwurf grobgranularer rekonfigurierbarer Bausteine ist das Thema dieser Dissertation. Im ersten Teil dieser Dissertation wird eine Sprache vorgestellt, die für die Beschreibung grobgranularer rekonfigurierbarer Systeme entwickelt wurde. Diese Sprache ermöglicht eine effiziente Spezifikation von Prozessoren-Arrays und die Beschreibung skalierbarer Netzwerkverbindungen. Der zweite Teil untersucht die Anpassung grobgranularer rekonfigurierbarer Bausteine an Anwendungssätze mittels spezialisierter Befehle. Methoden werden vorgestellt zur Erkennung und Extraktion von Operationsmustern aus einem Anwendungssatz. Diese Operationsmuster dienen dann zum Entwurf eines optimalen spezialisierten Befehlsatzes. Als Ergebnisse werden die Wirkungen von spezialisierten Befehlsätzen in grobgranularen Arrays analysiert und bewertet

University of Windsor Undergraduate Calendar 2002-2003

https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1010/thumbnail.jp