用多核CPU和多GPU加速描述逻辑的假设评估，提升归纳逻辑编程效率。
HT-HEDL: High-Throughput Hypothesis Evaluation in Description Logic
- 融合多核CPU与多GPU，分单个假设和批量假设两层加速计算。
- 单假设评估最高提速85倍（向量化多线程CPU），GPU单卡提速38倍。
- 批量评估可实现44倍吞吐量提升，适合大规模知识推理任务。
我们提出高通量描述逻辑假设评估引擎HT-HEDL，用于加速归纳逻辑编程中基于描述逻辑（DL）知识表示的假设评估计算，特别针对$ℓ{ALCQI}^{ℓ{(D)}}$语言。HT-HEDL通过整合多核CPU与多GPU的计算能力，在两个层面提升性能：一是单个假设评估，二是多个假设的批量评估。在单个假设评估中，采用单个GPU或向量化多线程CPU进行处理；其中，向量化多线程CPU将传统多线程与扩展向量指令集结合，性能相比传统多线程提升约85倍。使用单个GPU时，速度提升达约38倍。在批量评估中，通过并行调度多GPU与多核CPU，显著提高每秒评估的假设数量。实验表明，使用双GPU时吞吐量提升最高达29.3倍，结合向量化多线程CPU后可达约44倍。
原文摘要 · Abstract (English)
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.
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