12,965 research outputs found
High Throughput Push Based Storage Manager
The storage manager, as a key component of the database system, is
responsible for organizing, reading, and delivering data to the execution
engine for processing. According to the data serving mechanism, existing
storage managers are either pull-based, incurring high latency, or push-based,
leading to a high number of I/O requests when the CPU is busy. To improve these
shortcomings, this thesis proposes a push-based prefetching strategy in a
column-wise storage manager. The proposed strategy implements an efficient
cache layer to store shared data among queries to reduce the number of I/O
requests. The capacity of the cache is maintained by a time access-aware
eviction mechanism. Our strategy enables the storage manager to coordinate
multiple queries by merging their requests and dynamically generate an optimal
read order that maximizes the overall I/O throughput. We evaluated our storage
manager both over a disk-based redundant array of independent disks (RAID) and
an NVM Express (NVMe) solid-state drive (SSD). With the high read performance
of the SSD, we successfully minimized the total read time and number of I/O
accesses
HERA-B Framework for Online Calibration and Alignment
This paper describes the architecture and implementation of the HERA-B
framework for online calibration and alignment. At HERA-B the performance of
all trigger levels, including the online reconstruction, strongly depends on
using the appropriate calibration and alignment constants, which might change
during data taking. A system to monitor, recompute and distribute those
constants to online processes has been integrated in the data acquisition and
trigger systems.Comment: Submitted to NIM A. 4 figures, 15 page
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
From Cooperative Scans to Predictive Buffer Management
In analytical applications, database systems often need to sustain workloads
with multiple concurrent scans hitting the same table. The Cooperative Scans
(CScans) framework, which introduces an Active Buffer Manager (ABM) component
into the database architecture, has been the most effective and elaborate
response to this problem, and was initially developed in the X100 research
prototype. We now report on the the experiences of integrating Cooperative
Scans into its industrial-strength successor, the Vectorwise database product.
During this implementation we invented a simpler optimization of concurrent
scan buffer management, called Predictive Buffer Management (PBM). PBM is based
on the observation that in a workload with long-running scans, the buffer
manager has quite a bit of information on the workload in the immediate future,
such that an approximation of the ideal OPT algorithm becomes feasible. In the
evaluation on both synthetic benchmarks as well as a TPC-H throughput run we
compare the benefits of naive buffer management (LRU) versus CScans, PBM and
OPT; showing that PBM achieves benefits close to Cooperative Scans, while
incurring much lower architectural impact.Comment: VLDB201
Event Stream Processing with Multiple Threads
Current runtime verification tools seldom make use of multi-threading to
speed up the evaluation of a property on a large event trace. In this paper, we
present an extension to the BeepBeep 3 event stream engine that allows the use
of multiple threads during the evaluation of a query. Various parallelization
strategies are presented and described on simple examples. The implementation
of these strategies is then evaluated empirically on a sample of problems.
Compared to the previous, single-threaded version of the BeepBeep engine, the
allocation of just a few threads to specific portions of a query provides
dramatic improvement in terms of running time
Supporting simulation in industry through the application of grid computing
An increased need for collaborative research, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users access to geographically dispersed computing resources that are administered in multiple computer domains. The term grid computing, or grids, is popularly used to refer to such distributed systems. Simulation is characterized by the need to run multiple sets of computationally intensive experiments. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by users to model simulations in industry. It introduces our desktop grid, WinGrid, and presents a case study conducted at a leading European investment bank. Results indicate that grid computing does indeed hold promise for simulation in industry
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