DatalogMTL is an extension of Datalog with metric temporal operators that has
found applications in temporal ontology-based data access and query answering,
as well as in stream reasoning. Practical algorithms for DatalogMTL are reliant
on materialisation-based reasoning, where temporal facts are derived in a
forward chaining manner in successive rounds of rule applications. Current
materialisation-based procedures are, however, based on a naive evaluation
strategy, where the main source of inefficiency stems from redundant
computations.
In this paper, we propose a materialisation-based procedure which,
analogously to the classical seminaive algorithm in Datalog, aims at minimising
redundant computation by ensuring that each temporal rule instance is
considered at most once during the execution of the algorithm. Our experiments
show that our optimised seminaive strategy for DatalogMTL is able to
significantly reduce materialisation times.

本文提出了一种优化的 semianive DatalogMTL 策略，通过确保每个时态规则实例在算法执行期间最多只被考虑一次，从而在 DatalogMTL 中减少了冗余计算，显著缩短了材料化时间。

DatalogMTL 中的半朴素材料化

Seminaive Materialisation in DatalogMTL

Graph Convolutional Networks (GCNs) have drawn tremendous attention in the
past three years. Compared with other deep learning modalities,
high-performance hardware acceleration of GCNs is as critical but even more
challenging. The hurdles arise from the poor data locality and redundant
computation due to the large size, high sparsity, and irregular non-zero
distribution of real-world graphs.
In this paper we propose a novel hardware accelerator for GCN inference,
called I-GCN, that significantly improves data locality and reduces unnecessary
computation. The mechanism is a new online graph restructuring algorithm we
refer to as islandization. The proposed algorithm finds clusters of nodes with
strong internal but weak external connections. The islandization process yields
two major benefits. First, by processing islands rather than individual nodes,
there is better on-chip data reuse and fewer off-chip memory accesses. Second,
there is less redundant computation as aggregation for common/shared neighbors
in an island can be reused. The parallel search, identification, and leverage
of graph islands are all handled purely in hardware at runtime working in an
incremental pipeline. This is done without any preprocessing of the graph data
or adjustment of the GCN model structure.
Experimental results show that I-GCN can significantly reduce off-chip
accesses and prune 38% of aggregation operations, leading to performance
speedups over CPUs, GPUs, the prior art GCN accelerators of 5549x, 403x, and
5.7x on average, respectively.

本文提出了一种新的硬件加速器 I-GCN，采用在线图重构算法 Islandization，通过处理岛屿而不是单个节点，从而显著提高了数据局部性和减少了不必要的计算。I-GCN 可以减少 38% 的聚合操作，性能速度普遍快于 CPU、GPU 和先前的 GCN 加速器。