37 research outputs found
Formal Representation of the SS-DB Benchmark and Experimental Evaluation in EXTASCID
Evaluating the performance of scientific data processing systems is a
difficult task considering the plethora of application-specific solutions
available in this landscape and the lack of a generally-accepted benchmark. The
dual structure of scientific data coupled with the complex nature of processing
complicate the evaluation procedure further. SS-DB is the first attempt to
define a general benchmark for complex scientific processing over raw and
derived data. It fails to draw sufficient attention though because of the
ambiguous plain language specification and the extraordinary SciDB results. In
this paper, we remedy the shortcomings of the original SS-DB specification by
providing a formal representation in terms of ArrayQL algebra operators and
ArrayQL/SciQL constructs. These are the first formal representations of the
SS-DB benchmark. Starting from the formal representation, we give a reference
implementation and present benchmark results in EXTASCID, a novel system for
scientific data processing. EXTASCID is complete in providing native support
both for array and relational data and extensible in executing any user code
inside the system by the means of a configurable metaoperator. These features
result in an order of magnitude improvement over SciDB at data loading,
extracting derived data, and operations over derived data.Comment: 32 pages, 3 figure
Declarative Data Analytics: a Survey
The area of declarative data analytics explores the application of the
declarative paradigm on data science and machine learning. It proposes
declarative languages for expressing data analysis tasks and develops systems
which optimize programs written in those languages. The execution engine can be
either centralized or distributed, as the declarative paradigm advocates
independence from particular physical implementations. The survey explores a
wide range of declarative data analysis frameworks by examining both the
programming model and the optimization techniques used, in order to provide
conclusions on the current state of the art in the area and identify open
challenges.Comment: 36 pages, 2 figure
DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines
Integrated data analysis (IDA) pipelines—that combine data management (DM) and query processing, high-performance computing
(HPC), and machine learning (ML) training and scoring—become
increasingly common in practice. Interestingly, systems of these
areas share many compilation and runtime techniques, and the
used—increasingly heterogeneous—hardware infrastructure converges as well. Yet, the programming paradigms, cluster resource
management, data formats and representations, as well as execution
strategies differ substantially. DAPHNE is an open and extensible
system infrastructure for such IDA pipelines, including language abstractions, compilation and runtime techniques, multi-level scheduling, hardware (HW) accelerators, and computational storage for
increasing productivity and eliminating unnecessary overheads. In
this paper, we make a case for IDA pipelines, describe the overall
DAPHNE system architecture, its key components, and the design
of a vectorized execution engine for computational storage, HW
accelerators, as well as local and distributed operations. Preliminary experiments that compare DAPHNE with MonetDB, Pandas,
DuckDB, and TensorFlow show promising results