Today's production scale-out applications include many sub-application components, such as storage backends, logging infrastructure and AI models. These components have drastically different characteristics, are required to work in collaboration, and interface with each other as microservices. This leads to increasingly high complexity in developing, optimizing, configuring, and deploying scale-out applications, raising the barrier to entry for most individuals and small teams. We developed a novel co-designed runtime system, Jaseci, and programming language, Jac, which aims to reduce this complexity. The key design principle throughout Jaseci's design is to raise the level of abstraction by moving as much of the scale-out data management, microservice componentization, and live update complexity into the runtime stack to be automated and optimized automatically. We use real-world AI applications to demonstrate Jaseci's benefit for application performance and developer productivity.

Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scopei.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production taskoriented agent must handle. We evaluate a range of benchmark classifiers on our dataset along with several different out-of-scope identification schemes. We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries. Our dataset and evaluation fill an important gap in the field, offering a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.

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The class of modern datacenters recently coined as "warehouse scale computers" (WSCs) has traditionally been embraced as homogeneous computing platforms. However, due to frequent machine replacements and upgrades, modern WSCs are in fact composed of diverse commodity microarchitectures and machine configurations. Yet, current WSCs are designed with an assumption of homogeneity, leaving a potentially significant performance opportunity unexplored. In this paper, we investigate the key factors impacting the available heterogeneity in modern WSCs, and the benefit of exploiting this heterogeneity to maximize overall performance. We also introduce a new metric, opportunity factor, which can be used to quantify an application's sensitivity to the heterogeneity in a given WSC. For applications that are sensitive to heterogeneity, we observe a performance improvement of up to 70% when employing our approach. In a WSC composed of state-of-the-art machines, we can improve the overall performance of the entire datacenter by 16% over the status quo.

In this paper we study the impact of sharing memory resources on five Google datacenter applications: a web search engine, bigtable, content analyzer, image stitching, and protocol buffer. While prior work has found neither positive nor negative effects from cache sharing across the PARSEC benchmark suite, we find that across these datacenter applications, there is both a sizable benefit and a potential degradation from improperly sharing resources. In this paper, we first present a study of the importance of thread-tocore mappings for applications in the datacenter as threads can be mapped to share or to not share caches and bus bandwidth. Second, we investigate the impact of co-locating threads from multiple applications with diverse memory behavior and discover that the best mapping for a given application changes depending on its co-runner. Third, we investigate the application characteristics that impact performance in the various thread-to-core mapping scenarios. Finally, we present both a heuristics-based and an adaptive approach to arrive at good thread-to-core decisions in the datacenter. We observe performance swings of up to 25% for web search and 40% for other key applications, simply based on how application threads are mapped to cores. By employing our adaptive thread-to-core mapper, the performance of the datacenter applications presented in this work improved by up to 22% over status quo thread-to-core mapping and performs within 3% of optimal.

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