Current data-intensive systems suffer from scalability as they transfer massive amounts of data to the host DBMS to process it there. Novel near-data processing (NDP) DBMS architectures and smart storage can provably reduce the impact of raw data movement. However, transferring the result-set of an NDP operation may increase the data movement, and thus, the performance overhead. In this paper, we introduce a set of in-situ NDP result-set management techniques, such as spilling, materialization, and reuse. Our evaluation indicates a performance improvement of 1.13 × to 400 ×.

Abstract Massive data transfers in modern data-intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-Data processing (NDP) and a shift to code-to-data designs may represent a viable solution as packaging combinations of storage and compute elements on the same device has become feasible. The shift towards NDP system architectures calls for revision of established principles. Abstractions such as data formats and layouts typically spread multiple layers in traditional DBMS, the way they are processed is encapsulated within these layers of abstraction. The NDP-style processing requires an explicit definition of cross-layer data formats and accessors to ensure in-situ executions optimally utilizing the properties of the underlying NDP storage and compute elements. In this paper, we make the case for such data format definitions and investigate the performance benefits under RocksDB and the COSMOS hardware platform.

Today's Hybrid Transactional and Analytical Processing (HTAP) systems, tackle the ever-growing data in combination with a mixture of transactional and analytical workloads. While optimizing for aspects such as data freshness and performance isolation, they build on the traditional data-to-code principle and may trigger massive cold data transfers that impair the overall performance and scalability. Firstly, in this paper we show that Near-Data Processing (NDP) naturally fits in the HTAP design space. Secondly, we propose an NDP database architecture, allowing transactionally consistent in-situ executions of analytical operations in HTAP settings. We evaluate the proposed architecture in state-of-the-art key/value-stores and multi-versioned DBMS. In contrast to traditional setups, our approach yields robust, resource- and cost-efficient performance.

Many modern DBMS architectures require transferring data from storage to process it afterwards. Given the continuously increasing amounts of data, data transfers quickly become a scalability limiting factor. Near-Data Processing and smart/computational storage emerge as promising trends allowing for decoupled in-situ operation execution, data transfer reduction and better bandwidth utilization. However, not every operation is suitable for an in-situ execution and a careful placement and optimization is needed.

AuTODocK is a molecular docking application that consists of a genetic algorithm coupled with the Solis-Wets local-search method. Despite its wide usage, its power consumption on heterogeneous systems has not been evaluated extensively. In this work, we evaluate the energy efficiency of an OpenCL-accelerated version of AUTODOCK that, along with the traditional SolisWets method, newly incorporates the ADADELTA gradient-based local search. Executions on a Nvidia V100 GPU yielded energy efficiency improvements of up to 297x (Solis-Wets) and 137x (ADADELTA) with respect to the original AUTODOCK baseline.

Due to the ever-increasing computational demand of automotive applications, and in particular autonomous driving functionalities, the automotive industry and supply vendors are starting to adopt parallel and heterogeneous embedded platforms for their products.However, C and C++, the currently dominating programming languages in this industry, do not provide sufficient mechanisms to target such platforms. Established parallel programming models such as OpenMP and OpenCL on the other hand are tailored towards HPC systems.In this case study, we investigate the applicability of established parallel programming models to automotive workloads on heterogeneous platforms. We pursue a practical approach by re-enacting a typical development process for typical embedded platforms and representative benchmarks.