612 research outputs found
Massively Parallel Sort-Merge Joins in Main Memory Multi-Core Database Systems
Two emerging hardware trends will dominate the database system technology in
the near future: increasing main memory capacities of several TB per server and
massively parallel multi-core processing. Many algorithmic and control
techniques in current database technology were devised for disk-based systems
where I/O dominated the performance. In this work we take a new look at the
well-known sort-merge join which, so far, has not been in the focus of research
in scalable massively parallel multi-core data processing as it was deemed
inferior to hash joins. We devise a suite of new massively parallel sort-merge
(MPSM) join algorithms that are based on partial partition-based sorting.
Contrary to classical sort-merge joins, our MPSM algorithms do not rely on a
hard to parallelize final merge step to create one complete sort order. Rather
they work on the independently created runs in parallel. This way our MPSM
algorithms are NUMA-affine as all the sorting is carried out on local memory
partitions. An extensive experimental evaluation on a modern 32-core machine
with one TB of main memory proves the competitive performance of MPSM on large
main memory databases with billions of objects. It scales (almost) linearly in
the number of employed cores and clearly outperforms competing hash join
proposals - in particular it outperforms the "cutting-edge" Vectorwise parallel
query engine by a factor of four.Comment: VLDB201
Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS
GROMACS is a widely used package for biomolecular simulation, and over the
last two decades it has evolved from small-scale efficiency to advanced
heterogeneous acceleration and multi-level parallelism targeting some of the
largest supercomputers in the world. Here, we describe some of the ways we have
been able to realize this through the use of parallelization on all levels,
combined with a constant focus on absolute performance. Release 4.6 of GROMACS
uses SIMD acceleration on a wide range of architectures, GPU offloading
acceleration, and both OpenMP and MPI parallelism within and between nodes,
respectively. The recent work on acceleration made it necessary to revisit the
fundamental algorithms of molecular simulation, including the concept of
neighborsearching, and we discuss the present and future challenges we see for
exascale simulation - in particular a very fine-grained task parallelism. We
also discuss the software management, code peer review and continuous
integration testing required for a project of this complexity.Comment: EASC 2014 conference proceedin
PROTOTYPING THE SIMULATION OF A GATE LEVEL LOGIC APPLICATION PROGRAM INTERFACE (API) ON AN EXPLICIT-MULTI-THREADED (XMT) COMPUTER
Explicit-multi-threading (XMT) is a parallel programming approach for exploiting on-chip parallelism. Its fine-grained SPMD programming model is suitable for many computing intensive applications. In this paper, we present a parallel gate level logic simulation algorithm and study its implementation on an XMT processor. The test results show that hundreds-fold speedup can be achieved
PiCo: A Domain-Specific Language for Data Analytics Pipelines
In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models—for which only informal (and often confusing) semantics is generally provided—all share a common under- lying model, namely, the Dataflow model. Using this model as a starting point, it is possible to categorize and analyze almost all aspects about Big Data analytics tools from a high level perspective. This analysis can be considered as a first step toward a formal model to be exploited in the design of a (new) framework for Big Data analytics. By putting clear separations between all levels of abstraction (i.e., from the runtime to the user API), it is easier for a programmer or software designer to avoid mixing low level with high level aspects, as we are often used to see in state-of-the-art Big Data analytics frameworks.
From the user-level perspective, we think that a clearer and simple semantics is preferable, together with a strong separation of concerns. For this reason, we use the Dataflow model as a starting point to build a programming environment with a simplified programming model implemented as a Domain-Specific Language, that is on top of a stack of layers that build a prototypical framework for Big Data analytics.
The contribution of this thesis is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm, Google Dataflow), thus making it easier to understand high-level data-processing applications written in such frameworks. As result of this analysis, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level.
Second, we propose a programming environment based on such layered model in the form of a Domain-Specific Language (DSL) for processing data collections, called PiCo (Pipeline Composition). The main entity of this programming model is the Pipeline, basically a DAG-composition of processing elements. This model is intended to give the user an unique interface for both stream and batch processing, hiding completely data management and focusing only on operations, which are represented by Pipeline stages. Our DSL will be built on top of the FastFlow library, exploiting both shared and distributed parallelism, and implemented in C++11/14 with the aim of porting C++ into the Big Data world
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