Numeric simulations often generate large amounts of data that need to be stored or sent to other compute nodes. This paper investigates whether GPUs are powerful enough to make real-time data compression and decompression possible in such environments, that is, whether they can operate at the 32-or 40-Gb/s throughput of emerging network cards. The fastest parallel CPUbased floating-point data compression algorithm operates below 20 Gb/s on eight Xeon cores, which is significantly slower than the network speed and thus insufficient for compression to be practical in high-end networks. As a remedy, we have created the highly parallel GFC compression algorithm for double-precision floating-point data. This algorithm is specifically designed for GPUs. It compresses at a minimum of 75 Gb/s, decompresses at 90 Gb/s and above, and can therefore improve internode communication throughput on current and upcoming networks by fully saturating the interconnection links with compressed data.
Random-restart hill climbing is a common approach to combinatorial optimization problems such as the traveling salesman problem (TSP). We present and evaluate an implementation of random-restart hill climbing with 2-opt local search applied to TSP. Our implementation is capable of addressing large problem sizes at high throughput. It is based on the key insight that the GPU's hierarchical hardware parallelism is best exploited with a hierarchical implementation strategy, where independent climbs are parallelized between blocks and the 2-opt evaluations are parallelized across the threads within a block. We analyze the performance impact of this and other optimizations on our heuristic TSP solver and compare its performance to existing GPU-based 2-opt TSP solvers as well as a parallel CPU implementation. Our code outperforms the existing implementations by up to 3X, evaluating up to 60 billion 2-opt moves per second on a single K40 GPU. It also outperforms an OpenMP implementation run on 20 CPU cores by up to 8X.
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