{'fraction_non_alphanumeric': 0.017980636237897647, 'fraction_numerical': 0.0013831258644536654, 'mean_word_length': 5.350877192982456, '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}}
Exploiting heterogeneity in grid for deformable part models

Deformable part models (DPM) is a typical machine-learning based detection technique. It can achieve great success in detecting accuracy, but have compute-intensive tasks which severely restricts its utilization in many real world applications. In order to get high frame-rate for practical use, accelerators and grid computing infrastructure are needed. This paper propose a grid scheduling scheme which maintain two queues for GPUs and SW processors. The scheme obtain good scalability results through a lot of experiment. The experimental result on single processor node indicates that the parallel implementation on accelerators can achieve speedup up to 3x.