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.