10,513 research outputs found
PF-OLA: A High-Performance Framework for Parallel On-Line Aggregation
Online aggregation provides estimates to the final result of a computation
during the actual processing. The user can stop the computation as soon as the
estimate is accurate enough, typically early in the execution. This allows for
the interactive data exploration of the largest datasets. In this paper we
introduce the first framework for parallel online aggregation in which the
estimation virtually does not incur any overhead on top of the actual
execution. We define a generic interface to express any estimation model that
abstracts completely the execution details. We design a novel estimator
specifically targeted at parallel online aggregation. When executed by the
framework over a massive TPC-H instance, the estimator provides
accurate confidence bounds early in the execution even when the cardinality of
the final result is seven orders of magnitude smaller than the dataset size and
without incurring overhead.Comment: 36 page
Speculative Approximations for Terascale Analytics
Model calibration is a major challenge faced by the plethora of statistical
analytics packages that are increasingly used in Big Data applications.
Identifying the optimal model parameters is a time-consuming process that has
to be executed from scratch for every dataset/model combination even by
experienced data scientists. We argue that the incapacity to evaluate multiple
parameter configurations simultaneously and the lack of support to quickly
identify sub-optimal configurations are the principal causes. In this paper, we
develop two database-inspired techniques for efficient model calibration.
Speculative parameter testing applies advanced parallel multi-query processing
methods to evaluate several configurations concurrently. The number of
configurations is determined adaptively at runtime, while the configurations
themselves are extracted from a distribution that is continuously learned
following a Bayesian process. Online aggregation is applied to identify
sub-optimal configurations early in the processing by incrementally sampling
the training dataset and estimating the objective function corresponding to
each configuration. We design concurrent online aggregation estimators and
define halting conditions to accurately and timely stop the execution. We apply
the proposed techniques to distributed gradient descent optimization -- batch
and incremental -- for support vector machines and logistic regression models.
We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big
Data analytics system -- and evaluate their performance over terascale-size
synthetic and real datasets. The results confirm that as many as 32
configurations can be evaluated concurrently almost as fast as one, while
sub-optimal configurations are detected accurately in as little as a
fraction of the time
A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs
Sorting is at the core of many database operations, such as index creation,
sort-merge joins, and user-requested output sorting. As GPUs are emerging as a
promising platform to accelerate various operations, sorting on GPUs becomes a
viable endeavour. Over the past few years, several improvements have been
proposed for sorting on GPUs, leading to the first radix sort implementations
that achieve a sorting rate of over one billion 32-bit keys per second. Yet,
state-of-the-art approaches are heavily memory bandwidth-bound, as they require
substantially more memory transfers than their CPU-based counterparts.
Our work proposes a novel approach that almost halves the amount of memory
transfers and, therefore, considerably lifts the memory bandwidth limitation.
Being able to sort two gigabytes of eight-byte records in as little as 50
milliseconds, our approach achieves a 2.32-fold improvement over the
state-of-the-art GPU-based radix sort for uniform distributions, sustaining a
minimum speed-up of no less than a factor of 1.66 for skewed distributions.
To address inputs that either do not reside on the GPU or exceed the
available device memory, we build on our efficient GPU sorting approach with a
pipelined heterogeneous sorting algorithm that mitigates the overhead
associated with PCIe data transfers. Comparing the end-to-end sorting
performance to the state-of-the-art CPU-based radix sort running 16 threads,
our heterogeneous approach achieves a 2.06-fold and a 1.53-fold improvement for
sorting 64 GB key-value pairs with a skewed and a uniform distribution,
respectively.Comment: 16 pages, accepted at SIGMOD 201
Locality-Adaptive Parallel Hash Joins Using Hardware Transactional Memory
Previous work [1] has claimed that the best performing implementation of in-memory hash joins is based on (radix-)partitioning of the build-side input. Indeed, despite the overhead of partitioning, the benefits from increased cache-locality and synchronization free parallelism in the build-phase outweigh the costs when the input data is randomly ordered. However, many datasets already exhibit significant spatial locality (i.e., non-randomness) due to the way data items enter the database: through periodic ETL or trickle loaded in the form of transactions. In such cases, the first benefit of partitioning — increased locality — is largely irrelevant. In this paper, we demonstrate how hardware transactional memory (HTM) can render the other benefit, freedom from synchronization, irrelevant as well. Specifically, using careful analysis and engineering, we develop an adaptive hash join implementation that outperforms parallel radix-partitioned hash joins as well as sort-merge joins on data with high spatial locality. In addition, we show how, through lightweight (less than 1% overhead) runtime monitoring of the transaction abort rate, our implementation can detect inputs with low spatial locality and dynamically fall back to radix-partitioning of the build-side input. The result is a hash join implementation that is more than 3 times faster than the state-of-the-art on high-locality data and never more than 1% slower
Effects of distributed database modeling on evaluation of transaction rollbacks
Data distribution, degree of data replication, and transaction access patterns are key factors in determining the performance of distributed database systems. In order to simplify the evaluation of performance measures, database designers and researchers tend to make simplistic assumptions about the system. Here, researchers investigate the effect of modeling assumptions on the evaluation of one such measure, the number of transaction rollbacks in a partitioned distributed database system. The researchers developed six probabilistic models and expressions for the number of rollbacks under each of these models. Essentially, the models differ in terms of the available system information. The analytical results obtained are compared to results from simulation. It was concluded that most of the probabilistic models yield overly conservative estimates of the number of rollbacks. The effect of transaction commutativity on system throughput is also grossly undermined when such models are employed
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