We present High-Throughput Hypothesis Evaluation in Description Logic (HT-HEDL). HT-HEDL is a high-performance hypothesis evaluation engine that accelerates hypothesis evaluation computations for inductive logic programming (ILP) learners using description logic (DL) for their knowledge representation; in particular, HT-HEDL targets accelerating computations for the $\mathcal{ALCQI}^{\mathcal{(D)}}$ DL language. HT-HEDL aggregates the computing power of multi-core CPUs with multi-GPUs to improve hypothesis computations at two levels: 1) the evaluation of a single hypothesis and 2) the evaluation of multiple hypotheses (i.e., batch of hypotheses). In the first level, HT-HEDL uses a single GPU or a vectorized multi-threaded CPU to evaluate a single hypothesis. In vectorized multi-threaded CPU evaluation, classical (scalar) CPU multi-threading is combined with CPU's extended vector instructions set to extract more CPU-based performance. The experimental results revealed that HT-HEDL increased performance using CPU-based evaluation (on a single hypothesis): from 20.4 folds using classical multi-threading to $\sim85$ folds using vectorized multi-threading. In the GPU-based evaluation, HT-HEDL achieved speedups of up to $\sim38$ folds for single hypothesis evaluation using a single GPU. To accelerate the evaluation of multiple hypotheses, HT-HEDL combines, in parallel, GPUs with multi-core CPUs to increase evaluation throughput (number of evaluated hypotheses per second). The experimental results revealed that HT-HEDL increased evaluation throughput by up to 29.3 folds using two GPUs and up to $\sim44$ folds using two GPUs combined with a CPU's vectorized multi-threaded evaluation.

翻译：我们提出描述逻辑中的高通量假设评估（HT-HEDL）。HT-HEDL 是一种高性能假设评估引擎，它通过聚合多核 CPU 与多 GPU 的计算能力，在以下两个层面加速使用描述逻辑（DL）进行知识表示的归纳逻辑编程（ILP）学习器的假设评估计算：1）单个假设的评估；2）多个假设（即假设批次）的评估。在第一层面，HT-HEDL 使用单个 GPU 或向量化多线程 CPU 来评估单个假设。在向量化多线程 CPU 评估中，传统的（标量）CPU 多线程与 CPU 的扩展向量指令集相结合，以挖掘更多基于 CPU 的性能。实验结果表明，HT-HEDL 在使用基于 CPU 的评估（针对单个假设）时提升了性能：从使用传统多线程的 20.4 倍提升到使用向量化多线程的约 85 倍。在基于 GPU 的评估中，HT-HEDL 使用单个 GPU 进行单假设评估，实现了高达约 38 倍的加速。为了加速多个假设的评估，HT-HEDL 并行地结合 GPU 与多核 CPU，以提高评估吞吐量（每秒评估的假设数量）。实验结果表明，HT-HEDL 使用两个 GPU 将评估吞吐量提高了高达 29.3 倍，而使用两个 GPU 并结合 CPU 的向量化多线程评估时，提升高达约 44 倍。