Currently, we face the next major shift in processor designs that arose from the physical limitations known as the "dark silicon effect". Due to thermal limitations and shrinking transistor sizes, multi-core scaling is coming to an end. A major new direction that hardware vendors are currently investigating involves specialized and energy-efficient hardware accelerators (e.g., ASICs) placed on the same die as the normal CPU cores. In this paper, we present a novel query processing engine called SiliconDB that targets such heterogeneous processor environments. We leverage the Sparc M7 platform to develop and test our ideas. Based on the SSB benchmarks, as well as other micro benchmarks, we compare the efficiency of SiliconDB with existing execution strategies that make use of co-processors (e.g., FPGAs, GPUs) and demonstrate speed-up improvements of up to 2x.

In the last decade, the work centered around specialized co-processors for DBMSs has largely focused on efficient query processing algorithms for individual operators. However, a major limitation of existing co-processor systems is the PCI bottleneck, which severely limits the efficient use of this type of hardware in current systems.

Reusing intermediates in databases to speed-up analytical query processing was studied in prior work. Existing solutions require intermediate results of individual operators to be materialized using materialization operators. However, inserting such materialization operations into a query plan not only incurs additional execution costs but also often eliminates important cache- and register-locality opportunities, resulting in even higher performance penalties. This paper studies a novel reuse model for intermediates, which caches internal physical data structures materialized during query processing (due to pipeline breakers) and externalizes them so that they become reusable for upcoming operations. We focus on hash tables, the most commonly used internal data structure in main memory databases to perform join and aggregation operations. As queries arrive, our reuse-aware optimizer reasons about the reuse opportunities for hash tables, employing cost models that take into account hash table statistics together with the CPU and data movement costs within the cache hierarchy. Experimental results, based on our prototype implementation, demonstrate performance gains of 2x for typical analytical workloads with no additional overhead for materializing intermediates.

Data analytics has recently grown to include increasingly sophisticated techniques, such as machine learning and advanced statistics. Users frequently express these complex analytics tasks as workflows of user-defined functions (UDFs) that specify each algorithmic step. However, given typical hardware configurations and dataset sizes, the core challenge of complex analytics is no longer sheer data volume but rather the computation itself, and the next generation of analytics frameworks must focus on optimizing for this computation bottleneck. While query compilation has gained widespread popularity as a way to tackle the computation bottleneck for traditional SQL workloads, relatively little work addresses UDF-centric workflows in the domain of complex analytics. In this paper, we describe a novel architecture for automatically compiling workflows of UDFs. We also propose several optimizations that consider properties of the data, UDFs, and hardware together in order to generate different code on a case-by-case basis. To evaluate our approach, we implemented these techniques in T upleware , a new high-performance distributed analytics system, and our benchmarks show performance improvements of up to three orders of magnitude compared to alternative systems.

Multi objective clustering is one focused area of multi objective optimization. Multi objective optimization attracted many researchers in several areas over a decade. Utilizing multi objective clustering mainly considers multiple objectives simultaneously and results with several natural clustering solutions. Obtained result set suggests different point of views for solving the clustering problem. This paper assumes all potential solutions belong to different experts and in overall, ensemble of solutions finally has been utilized for finding the final natural clustering. We have tested on categorical, further on mixed credit card dataset with different objectives, and compared them against single objective clustering result in terms of purity.