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The evolution of computer algorithms and micro-architectures continuously leads to novel applications and solutions that explore modern computers’ potential. A drawback of this development is the rising complexity of hardware-oriented tests during software development. This brief proposes Hambug, a method to support developers during software testing of parallel applications in CPU-FPGA environments. The main component is a run-time memory analyzer that employs shared channels. Thus, Hambug enables memory analysis of parallel applications without influencing the CPU using a hardware-based debug module. Experimental results indicate that this method presents lower slowdown compared with modern dynamic analysis tools. [ABSTRACT FROM AUTHOR]

Coarse grain reconfigurable architectures (CGRAs) are an emerging hybrid computational architecture that has the parallel customization benefits of low-level logic devices, such as FPGAs and ASICs, while the relative coarseness of these architectures makes CGRAs easier to design for, which is more similar to the traditional processor. In the process of mapping designs to CGRAs, flexible, fast, and adaptive placement and routing (P&R) is fundamental in order to implement efficient run-time reconfigurable frameworks. It is well-known that P&R is an NP-complete problem, and thus, solutions rely on heuristics to achieve quality results with acceptable execution times. CGRA P&R has different constraints compared to traditional VLSI P&R, e.g., path latency balancing and modulo scheduling of loops. In this work, we propose a graph-based P&R approach that uses graph traversals to map designs to CGRAs. Additionally, we parallelize our approach with a graph-based greedy heuristic that executes on a GPU. We compare our proposed P&R approach with the CGRA-ME framework, which implements simulated annealing and integer linear programming placement algorithms. Our results show that this new approach can generate optimal mappings and improve the execution run-time up to several orders of magnitude. Furthermore, considering spatial mapping at the millisecond scale, our GPU approach is one order of magnitude faster compared to the state-of-the-art tool VPR. [ABSTRACT FROM AUTHOR]