{'fraction_non_alphanumeric': 0.03683640303358613, 'fraction_numerical': 0.02275189599133261, 'mean_word_length': 4.668711656441718, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
SPMario: Scale up MapReduce with I/O-Oriented Scheduling for the GPU

The popularity of GPUs in general purpose computation has prompted efforts to scale up MapReduce systems with GPUs, but lack of efficient I/O handling results in underutilization of shared system resources in existing systems. This paper presents SPMario, a scale-up GPU MapReduce framework to speed up job execution and boost utilization of system resources with the new I/O Oriented Scheduling. The evaluation on a set of representative benchmarks against a highly-optimized baseline system shows that for the single job cases, SPMario can speedup job execution by up to 2.28×, and boost GPU utilization by 2.12× and 2.51× for I/O utilization. When scheduling two jobs together, I/O Oriented Scheduling outperforms round-robin scheduling by up to 13.54% in total execution time, and by up to 12.27% and 14.92% in GPU and I/O utilization, respectively.