CRoute: a fast high-quality timing-driven connection-based FPGA router

FPGA routing is an important part of physical design as the programmable interconnection network requires the majority of the total silicon area and the connections largely contribute to delay and power. It should also occur with minimum runtime to enable efficient design exploration. In this work we elaborate on the concept of the connection-based routing principle. The algorithm is improved and a timing-driven version is introduced. The router, called CROUTE, is implemented in an easy to adapt FPGA CAD framework written in Java, which is publicly available on GitHub. Quality and runtime are compared to the state-of-the-art router in VPR 7.0.7. Benchmarking is done with the TITAN23 design suite, which consists of large heterogeneous designs targeted to a detailed representation of the Stratix IV FPGA. CROUTE gains in both the total wirelength and maximum clock frequency while reducing the routing runtime. The total wire-length reduces by 11% and the maximum clock frequency increases by 6%. These high-quality results are obtained in 3.4x less routing runtime

Accelerating FPGA routing through algorithmic enhancements and connection-aware parallelization

Routing is a crucial step in Field Programmable Gate Array (FPGA) physical design, as it determines the routes of signals in the circuit, which impacts the design implementation quality significantly. It can be very time-consuming to successfully route all the signals of large circuits that utilize many FPGA resources. Attempts have been made to shorten the routing runtime for efficient design exploration while expecting high-quality implementations. In this work, we elaborate on the connection-based routing strategy and algorithmic enhancements to improve the serial FPGA routing. We also explore a recursive partitioning-based parallelization technique to further accelerate the routing process. To exploit more parallelism by a finer granularity in both spatial partitioning and routing, a connection-aware routing bounding box model is proposed for the source-sink connections of the nets. It is built upon the location information of each connection’s source, sink, and the geometric center of the net that the connection belongs to, different from the existing net-based routing bounding box that covers all the pins of the entire net. We present that the proposed connection-aware routing bounding box is more beneficial for parallel routing than the existing net-based routing bounding box. The quality and runtime of the serial and multi-threaded routers are compared to the router in VPR 7.0.7. The large heterogeneous Titan23 designs that are targeted to a detailed representation of the Stratix IV FPGA are used for benchmarking. With eight threads, the parallel router using the connection-aware routing bounding box model reaches a speedup of 6.1× over the serial router in VPR 7.0.7, which is 1.24× faster than the one using the existing net-based routing bounding box model, while reducing the total wire-length by 10% and the critical path delay by 7%

Reconfigurable computing for large-scale graph traversal algorithms

This thesis proposes a reconfigurable computing approach for supporting parallel processing in large-scale graph traversal algorithms. Our approach is based on a reconfigurable hardware architecture which exploits the capabilities of both FPGAs (Field-Programmable Gate Arrays) and a multi-bank parallel memory subsystem. The proposed methodology to accelerate graph traversal algorithms has been applied to three case studies, revealing that application-specific hardware customisations can benefit performance. A summary of our four contributions is as follows. First, a reconfigurable computing approach to accelerate large-scale graph traversal algorithms. We propose a reconfigurable hardware architecture which decouples computation and communication while keeping multiple memory requests in flight at any given time, taking advantage of the high bandwidth of multi-bank memory subsystems. Second, a demonstration of the effectiveness of our approach through two case studies: the breadth-first search algorithm, and a graphlet counting algorithm from bioinformatics. Both case studies involve graph traversal, but each of them adopts a different graph data representation. Third, a method for using on-chip memory resources in FPGAs to reduce off-chip memory accesses for accelerating graph traversal algorithms, through a case-study of the All-Pairs Shortest-Paths algorithm. This case study has been applied to process human brain network data. Fourth, an evaluation of an approach based on instruction-set extension for FPGA design against many-core GPUs (Graphics Processing Units), based on a set of benchmarks with different memory access characteristics. It is shown that while GPUs excel at streaming applications, the proposed approach can outperform GPUs in applications with poor locality characteristics, such as graph traversal problems.Open Acces

High performance graph analysis on parallel architectures

PhD ThesisOver the last decade pharmacology has been developing computational methods to enhance drug development and testing. A computational method called network pharmacology uses graph analysis tools to determine protein target sets that can lead on better targeted drugs for diseases as Cancer. One promising area of network-based pharmacology is the detection of protein groups that can produce better e ects if they are targeted together by drugs. However, the e cient prediction of such protein combinations is still a bottleneck in the area of computational biology. The computational burden of the algorithms used by such protein prediction strategies to characterise the importance of such proteins consists an additional challenge for the eld of network pharmacology. Such computationally expensive graph algorithms as the all pairs shortest path (APSP) computation can a ect the overall drug discovery process as needed network analysis results cannot be given on time. An ideal solution for these highly intensive computations could be the use of super-computing. However, graph algorithms have datadriven computation dictated by the structure of the graph and this can lead to low compute capacity utilisation with execution times dominated by memory latency. Therefore, this thesis seeks optimised solutions for the real-world graph problems of critical node detection and e ectiveness characterisation emerged from the collaboration with a pioneer company in the eld of network pharmacology as part of a Knowledge Transfer Partnership (KTP) / Secondment (KTS). In particular, we examine how genetic algorithms could bene t the prediction of protein complexes where their removal could produce a more e ective 'druggable' impact. Furthermore, we investigate how the problem of all pairs shortest path (APSP) computation can be bene ted by the use of emerging parallel hardware architectures as GPU- and FPGA- desktop-based accelerators. In particular, we address the problem of critical node detection with the development of a heuristic search method. It is based on a genetic algorithm that computes optimised node combinations where their removal causes greater impact than common impact analysis strategies. Furthermore, we design a general pattern for parallel network analysis on multi-core architectures that considers graph's embedded properties. It is a divide and conquer approach that decomposes a graph into smaller subgraphs based on its strongly connected components and computes the all pairs shortest paths concurrently on GPU. Furthermore, we use linear algebra to design an APSP approach based on the BFS algorithm. We use algebraic expressions to transform the problem of path computation to multiple independent matrix-vector multiplications that are executed concurrently on FPGA. Finally, we analyse how the optimised solutions of perturbation analysis and parallel graph processing provided in this thesis will impact the drug discovery process.This research was part of a Knowledge Transfer Partnership (KTP) and Knowledge Transfer Secondment (KTS) between e-therapeutics PLC and Newcastle University. It was supported as a collaborative project by e-therapeutics PLC and Technology Strategy boar

FPGA-based Query Acceleration for Non-relational Databases

Database management systems are an integral part of today’s everyday life. Trends like smart applications, the internet of things, and business and social networks require applications to deal efficiently with data in various data models close to the underlying domain. Therefore, non-relational database systems provide a wide variety of database models, like graphs and documents. However, current non-relational database systems face performance challenges due to the end of Dennard scaling and therefore performance scaling of CPUs. In the meanwhile, FPGAs have gained traction as accelerators for data management. Our goal is to tackle the performance challenges of non-relational database systems with FPGA acceleration and, at the same time, address design challenges of FPGA acceleration itself. Therefore, we split this thesis up into two main lines of work: graph processing and flexible data processing. Because of the lacking benchmark practices for graph processing accelerators, we propose GraphSim. GraphSim is able to reproduce runtimes of these accelerators based on a memory access model of the approach. Through this simulation environment, we extract three performance-critical accelerator properties: asynchronous graph processing, compressed graph data structure, and multi-channel memory. Since these accelerator properties have not been combined in one system, we propose GraphScale. GraphScale is the first scalable, asynchronous graph processing accelerator working on a compressed graph and outperforms all state-of-the-art graph processing accelerators. Focusing on accelerator flexibility, we propose PipeJSON as the first FPGA-based JSON parser for arbitrary JSON documents. PipeJSON is able to achieve parsing at line-speed, outperforming the fastest, vectorized parsers for CPUs. Lastly, we propose the subgraph query processing accelerator GraphMatch which outperforms state-of-the-art CPU systems for subgraph query processing and is able to flexibly switch queries during runtime in a matter of clock cycles