Modern analytics applications use a diverse mix of libraries and functions. Unfortunately, there is no optimization across these libraries, resulting in performance penalties as high as an order of magnitude in many applications. To address this problem, we proposed Weld, a common runtime for existing data analytics libraries that performs key physical optimizations such as pipelining under existing, imperative library APIs. In this work, we further develop the Weld vision by designing an automatic adaptive optimizer for Weld applications, and evaluating its impact on realistic data science workloads. Our optimizer eliminates multiple forms of overhead that arise when composing imperative libraries like Pandas and NumPy, and uses lightweight measurements to make data-dependent decisions at runtime in ad-hoc workloads where no statistics are available, with sub-second overhead. We also evaluate which optimizations have the largest impact in practice and whether Weld can be integrated into libraries incrementally. Our results are promising: using our optimizer, Weld accelerates data science workloads by up to 23× on one thread and 80× on eight threads, and its adaptive optimizations provide up to a 3.75× speedup over rule-based optimization. Moreover, Weld provides benefits if even just 4-5 operators in a library are ported to use it. Our results show that common runtime designs like Weld may be a viable approach to accelerate analytics.

By moving network appliance functionality from proprietary hardware to software, Network Function Virtualization promises to bring the advantages of cloud computing to network packet processing. However, the evolution of cloud computing (particularly for data analytics) has greatly benefited from application-independent methods for scaling and placement that achieve high efficiency while relieving programmers of these burdens. NFV has no such general management solutions. In this paper, we present a scalable and application-agnostic scheduling framework for packet processing, and compare its performance to current approaches.

Exploratory big data applications often run on raw unstructured or semi-structured data formats, such as JSON files or text logs. These applications can spend 80-90% of their execution time parsing the data. In this paper, we propose a new approach for reducing this overhead: apply filters on the data's raw bytestream before parsing. This technique, which we call raw filtering, leverages the features of modern hardware and the high selectivity of queries found in many exploratory applications. With raw filtering, a user-specified query predicate is compiled into a set of filtering primitives called raw filters (RFs). RFs are fast, SIMD-based operators that occasionally yield false positives, but never false negatives. We combine multiple RFs into an RF cascade to decrease the false positive rate and maximize parsing throughput. Because the best RF cascade is datadependent, we propose an optimizer that dynamically selects the combination of RFs with the best expected throughput, achieving within 10% of the global optimum cascade while adding less than 1.2% overhead. We implement these techniques in a system called Sparser, which automatically manages a parsing cascade given a data stream in a supported format (e.g., JSON, Avro, Parquet) and a user query. We show that many real-world applications are highly selective and benefit from Sparser. Across diverse workloads, Sparser accelerates state-of-the-art parsers such as Mison by up to 22× and improves end-to-end application performance by up to 9×.

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