1,871 research outputs found
GPUs as Storage System Accelerators
Massively multicore processors, such as Graphics Processing Units (GPUs),
provide, at a comparable price, a one order of magnitude higher peak
performance than traditional CPUs. This drop in the cost of computation, as any
order-of-magnitude drop in the cost per unit of performance for a class of
system components, triggers the opportunity to redesign systems and to explore
new ways to engineer them to recalibrate the cost-to-performance relation. This
project explores the feasibility of harnessing GPUs' computational power to
improve the performance, reliability, or security of distributed storage
systems. In this context, we present the design of a storage system prototype
that uses GPU offloading to accelerate a number of computationally intensive
primitives based on hashing, and introduce techniques to efficiently leverage
the processing power of GPUs. We evaluate the performance of this prototype
under two configurations: as a content addressable storage system that
facilitates online similarity detection between successive versions of the same
file and as a traditional system that uses hashing to preserve data integrity.
Further, we evaluate the impact of offloading to the GPU on competing
applications' performance. Our results show that this technique can bring
tangible performance gains without negatively impacting the performance of
concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201
Big Data Caching for Networking: Moving from Cloud to Edge
In order to cope with the relentless data tsunami in wireless networks,
current approaches such as acquiring new spectrum, deploying more base stations
(BSs) and increasing nodes in mobile packet core networks are becoming
ineffective in terms of scalability, cost and flexibility. In this regard,
context-aware G networks with edge/cloud computing and exploitation of
\emph{big data} analytics can yield significant gains to mobile operators. In
this article, proactive content caching in G wireless networks is
investigated in which a big data-enabled architecture is proposed. In this
practical architecture, vast amount of data is harnessed for content popularity
estimation and strategic contents are cached at the BSs to achieve higher
users' satisfaction and backhaul offloading. To validate the proposed solution,
we consider a real-world case study where several hours of mobile data traffic
is collected from a major telecom operator in Turkey and a big data-enabled
analysis is carried out leveraging tools from machine learning. Based on the
available information and storage capacity, numerical studies show that several
gains are achieved both in terms of users' satisfaction and backhaul
offloading. For example, in the case of BSs with of content ratings
and Gbyte of storage size ( of total library size), proactive
caching yields of users' satisfaction and offloads of the
backhaul.Comment: accepted for publication in IEEE Communications Magazine, Special
Issue on Communications, Caching, and Computing for Content-Centric Mobile
Network
On the traffic offloading in Wi-Fi supported heterogeneous wireless networks
Heterogeneous small cell networks (HetSNet) comprise several low power, low cost (SBSa), (D2D) enabled links wireless-fidelity (Wi-Fi) access points (APs) to support the existing macrocell infrastructure, decrease over the air signaling and energy consumption, and increase network capacity, data rate and coverage. This paper presents an active user dependent path loss (PL) based traffic offloading (TO) strategy for HetSNets and a comparative study on two techniques to offload the traffic from macrocell to (SBSs) for indoor environments: PL and signal-to-interference ratio (SIR) based strategies. To quantify the improvements, the PL based strategy against the SIR based strategy is compared while considering various macrocell and (SBS) coverage areas and traffic–types. On the other hand, offloading in a dense urban setting may result in overcrowding the (SBSs). Therefore, hybrid traffic–type driven offloading technologies such as (WiFi) and (D2D) were proposed to en route the delay tolerant applications through (WiFi) (APs) and (D2D) links. It is necessary to illustrate the impact of daily user traffic profile, (SBSs) access schemes and traffic–type while deciding how much of the traffic should be offloaded to (SBSs). In this context, (AUPF) is introduced to account for the population of active small cells which depends on the variable traffic load due to the active users
GPU Accelerated Particle Visualization with Splotch
Splotch is a rendering algorithm for exploration and visual discovery in
particle-based datasets coming from astronomical observations or numerical
simulations. The strengths of the approach are production of high quality
imagery and support for very large-scale datasets through an effective mix of
the OpenMP and MPI parallel programming paradigms. This article reports our
experiences in re-designing Splotch for exploiting emerging HPC architectures
nowadays increasingly populated with GPUs. A performance model is introduced
for data transfers, computations and memory access, to guide our re-factoring
of Splotch. A number of parallelization issues are discussed, in particular
relating to race conditions and workload balancing, towards achieving optimal
performances. Our implementation was accomplished by using the CUDA programming
paradigm. Our strategy is founded on novel schemes achieving optimized data
organisation and classification of particles. We deploy a reference simulation
to present performance results on acceleration gains and scalability. We
finally outline our vision for future work developments including possibilities
for further optimisations and exploitation of emerging technologies.Comment: 25 pages, 9 figures. Astronomy and Computing (2014
EbbRT: a customizable operating system for cloud applications
Efficient use of hardware requires operating system components be customized to the application workload. Our general purpose operating systems are ill-suited for this task. We present Genesis, a new operating system that enables per-application customizations for cloud applications. Genesis achieves this through a novel heterogeneous distributed structure, a partitioned object model, and an event-driven execution environment. This paper describes the design and prototype implementation of Genesis, and evaluates its ability to improve the performance of common cloud applications. The evaluation of the Genesis prototype demonstrates memcached, run within a VM, can outperform memcached run on an unvirtualized Linux. The prototype evaluation also demonstrates an 14% performance improvement of a V8 JavaScript engine benchmark, and a node.js webserver that achieves a 50% reduction in 99th percentile latency compared to it run on Linux
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