A range of explanation engines assist data analysts by performing feature selection over increasingly high-volume and high-dimensional data, grouping and highlighting commonalities among data points. While useful in diverse tasks such as user behavior analytics, operational event processing, and root cause analysis, today's explanation engines are designed as standalone data processing tools that do not interoperate with traditional, SQL-based analytics workflows; this limits the applicability and extensibility of these engines. In response, we propose the DIFF operator, a relational aggregation operator that unifies the core functionality of these engines with declarative relational query processing. We implement both single-node and distributed versions of the DIFF operator in MB SQL, an extension of MacroBase, and demonstrate how DIFF can provide the same semantics as existing explanation engines while capturing a broad set of production use cases in industry, including at Microsoft and Facebook. Additionally, we illustrate how this declarative approach to data explanation enables new logical and physical query optimizations. We evaluate these optimizations on several real-world production applications, and find that DIFF in MB SQL can outperform state-of-the-art engines by up to an order of magnitude.

In many workloads, 25% to 50% of instructions have slack allowing them to be delayed without impacting performance. To exploit this slack, processors may implement more power-efficient, longer latency pipelines or provide dynamically scaled pipelines using multiple clock domains. Issuing instructions with slack to slower pipelines can result in substantial power savings, with minimal performance loss. Considering both dynamic and static power dissipation, we found that by using longer latency pipelines the power of functional unit pipelines decreases by 20% to 55% with a performance impact of 0% to 3% for SPEC2000 and MediaBench workloads. Dynamic scaling reduces the performance loss in intense multimedia workloads by up to 2%, but achieves lower power savings.