1,153 research outputs found
AT-GIS: highly parallel spatial query processing with associative transducers
Users in many domains, including urban planning, transportation, and environmental science want to execute analytical queries over continuously updated spatial datasets. Current solutions for largescale spatial query processing either rely on extensions to RDBMS, which entails expensive loading and indexing phases when the data changes, or distributed map/reduce frameworks, running on resource-hungry compute clusters. Both solutions struggle with the sequential bottleneck of parsing complex, hierarchical spatial data formats, which frequently dominates query execution time. Our goal is to fully exploit the parallelism offered by modern multicore CPUs for parsing and query execution, thus providing the performance of a cluster with the resources of a single machine. We describe AT-GIS, a highly-parallel spatial query processing system that scales linearly to a large number of CPU cores. ATGIS integrates the parsing and querying of spatial data using a new computational abstraction called associative transducers(ATs). ATs can form a single data-parallel pipeline for computation without requiring the spatial input data to be split into logically independent blocks. Using ATs, AT-GIS can execute, in parallel, spatial query operators on the raw input data in multiple formats, without any pre-processing. On a single 64-core machine, AT-GIS provides 3× the performance of an 8-node Hadoop cluster with 192 cores for containment queries, and 10× for aggregation queries
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
ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads
ARM processors have dominated the mobile device market in the last decade due
to their favorable computing to energy ratio. In this age of Cloud data centers
and Big Data analytics, the focus is increasingly on power efficient
processing, rather than just high throughput computing. ARM's first commodity
server-grade processor is the recent AMD A1100-series processor, based on a
64-bit ARM Cortex A57 architecture. In this paper, we study the performance and
energy efficiency of a server based on this ARM64 CPU, relative to a comparable
server running an AMD Opteron 3300-series x64 CPU, for Big Data workloads.
Specifically, we study these for Intel's HiBench suite of web, query and
machine learning benchmarks on Apache Hadoop v2.7 in a pseudo-distributed
setup, for data sizes up to files, web pages and tuples. Our
results show that the ARM64 server's runtime performance is comparable to the
x64 server for integer-based workloads like Sort and Hive queries, and only
lags behind for floating-point intensive benchmarks like PageRank, when they do
not exploit data parallelism adequately. We also see that the ARM64 server
takes the energy, and has an Energy Delay Product (EDP) that
is lower than the x64 server. These results hold promise for ARM64
data centers hosting Big Data workloads to reduce their operational costs,
while opening up opportunities for further analysis.Comment: Accepted for publication in the Proceedings of the 24th IEEE
International Conference on High Performance Computing, Data, and Analytics
(HiPC), 201
Acceleration of Coarse Grain Molecular Dynamics on GPU Architectures
Coarse grain (CG) molecular models have been proposed to simulate complex sys- tems with lower computational overheads and longer timescales with respect to atom- istic level models. However, their acceleration on parallel architectures such as Graphic Processing Units (GPU) presents original challenges that must be carefully evaluated. The objective of this work is to characterize the impact of CG model features on parallel simulation performance. To achieve this, we implemented a GPU-accelerated version of a CG molecular dynamics simulator, to which we applied specic optimizations for CG models, such as dedicated data structures to handle dierent bead type interac- tions, obtaining a maximum speed-up of 14 on the NVIDIA GTX480 GPU with Fermi architecture. We provide a complete characterization and evaluation of algorithmic and simulated system features of CG models impacting the achievable speed-up and accuracy of results, using three dierent GPU architectures as case studie
Performance Characterization of NVMe Flash Devices with Zoned Namespaces (ZNS)
The recent emergence of NVMe flash devices with Zoned Namespace support, ZNS
SSDs, represents a significant new advancement in flash storage. ZNS SSDs
introduce a new storage abstraction of append-only zones with a set of new I/O
(i.e., append) and management (zone state machine transition) commands. With
the new abstraction and commands, ZNS SSDs offer more control to the host
software stack than a non-zoned SSD for flash management, which is known to be
complex (because of garbage collection, scheduling, block allocation,
parallelism management, overprovisioning). ZNS SSDs are, consequently, gaining
adoption in a variety of applications (e.g., file systems, key-value stores,
and databases), particularly latency-sensitive big-data applications. Despite
this enthusiasm, there has yet to be a systematic characterization of ZNS SSD
performance with its zoned storage model abstractions and I/O operations. This
work addresses this crucial shortcoming. We report on the performance features
of a commercially available ZNS SSD (13 key observations), explain how these
features can be incorporated into publicly available state-of-the-art ZNS
emulators, and recommend guidelines for ZNS SSD application developers. All
artifacts (code and data sets) of this study are publicly available at
https://github.com/stonet-research/NVMeBenchmarks.Comment: Paper to appear in the https://clustercomp.org/2023/program
Performance Characterization of NVMe Flash Devices with Zoned Namespaces (ZNS)
The recent emergence of NVMe flash devices with Zoned Namespace support, ZNS SSDs, represents a significant new advancement in flash storage. ZNS SSDs introduce a new storage abstraction of append-only zones with a set of new I/O (i.e., append) and management (zone state machine transition) commands. With the new abstraction and commands, ZNS SSDs offer more control to the host software stack than a non-zoned SSD for flash management, which is known to be complex (because of garbage collection, scheduling, block allocation, parallelism management, overprovisioning). ZNS SSDs are, consequently, gaining adoption in a variety of applications (e.g., file systems, key-value stores, and databases), particularly latency-sensitive big-data applications. Despite this enthusiasm, there has yet to be a systematic characterization of ZNS SSD performance with its zoned storage model abstractions and I/O operations. This work addresses this crucial shortcoming. We report on the performance features of a commercially available ZNS SSD (13 key observations), explain how these features can be incorporated into publicly available state-of-the-art ZNS emulators, and recommend guidelines for ZNS SSD application developers. All artifacts (code and data sets) of this study are publicly available at https://github.com/stonet-research/NVMeBenchmarks
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