8,025 research outputs found
Gauge Field Generation on Large-Scale GPU-Enabled Systems
Over the past years GPUs have been successfully applied to the task of
inverting the fermion matrix in lattice QCD calculations. Even strong scaling
to capability-level supercomputers, corresponding to O(100) GPUs or more has
been achieved. However strong scaling a whole gauge field generation algorithm
to this regim requires significantly more functionality than just having the
matrix inverter utilizing the GPUs and has not yet been accomplished. This
contribution extends QDP-JIT, the migration of SciDAC QDP++ to GPU-enabled
parallel systems, to help to strong scale the whole Hybrid Monte-Carlo to this
regime. Initial results are shown for gauge field generation with Chroma
simulating pure Wilson fermions on OLCF TitanDev.Comment: The 30th International Symposium on Lattice Field Theory, June 24-29,
2012, Cairns, Australia (Acknowledgment and Citation added
Enabling a High Throughput Real Time Data Pipeline for a Large Radio Telescope Array with GPUs
The Murchison Widefield Array (MWA) is a next-generation radio telescope
currently under construction in the remote Western Australia Outback. Raw data
will be generated continuously at 5GiB/s, grouped into 8s cadences. This high
throughput motivates the development of on-site, real time processing and
reduction in preference to archiving, transport and off-line processing. Each
batch of 8s data must be completely reduced before the next batch arrives.
Maintaining real time operation will require a sustained performance of around
2.5TFLOP/s (including convolutions, FFTs, interpolations and matrix
multiplications). We describe a scalable heterogeneous computing pipeline
implementation, exploiting both the high computing density and FLOP-per-Watt
ratio of modern GPUs. The architecture is highly parallel within and across
nodes, with all major processing elements performed by GPUs. Necessary
scatter-gather operations along the pipeline are loosely synchronized between
the nodes hosting the GPUs. The MWA will be a frontier scientific instrument
and a pathfinder for planned peta- and exascale facilities.Comment: Version accepted by Comp. Phys. Com
ASCR/HEP Exascale Requirements Review Report
This draft report summarizes and details the findings, results, and
recommendations derived from the ASCR/HEP Exascale Requirements Review meeting
held in June, 2015. The main conclusions are as follows. 1) Larger, more
capable computing and data facilities are needed to support HEP science goals
in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of
the demand at the 2025 timescale is at least two orders of magnitude -- and in
some cases greater -- than that available currently. 2) The growth rate of data
produced by simulations is overwhelming the current ability, of both facilities
and researchers, to store and analyze it. Additional resources and new
techniques for data analysis are urgently needed. 3) Data rates and volumes
from HEP experimental facilities are also straining the ability to store and
analyze large and complex data volumes. Appropriately configured
leadership-class facilities can play a transformational role in enabling
scientific discovery from these datasets. 4) A close integration of HPC
simulation and data analysis will aid greatly in interpreting results from HEP
experiments. Such an integration will minimize data movement and facilitate
interdependent workflows. 5) Long-range planning between HEP and ASCR will be
required to meet HEP's research needs. To best use ASCR HPC resources the
experimental HEP program needs a) an established long-term plan for access to
ASCR computational and data resources, b) an ability to map workflows onto HPC
resources, c) the ability for ASCR facilities to accommodate workflows run by
collaborations that can have thousands of individual members, d) to transition
codes to the next-generation HPC platforms that will be available at ASCR
facilities, e) to build up and train a workforce capable of developing and
using simulations and analysis to support HEP scientific research on
next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
MorphoSys: efficient colocation of QoS-constrained workloads in the cloud
In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for unencumbered use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may result in inefficient utilization of the host’s resources. In this paper, we propose that periodic resource allocation and consumption models -- often used to characterize real-time workloads -- be used for a more granular expression of SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the infrastructure provider to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that goal, we present MORPHOSYS: a framework for a service that allows the manipulation of SLAs to enable efficient colocation of arbitrary workloads in a dynamic setting. We present results from extensive trace-driven simulations of colocated Video-on-Demand servers in a cloud setting. These results show that potentially-significant reduction in wasted resources (by as much as 60%) are possible using MORPHOSYS.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798
SARA: Self-Aware Resource Allocation for Heterogeneous MPSoCs
In modern heterogeneous MPSoCs, the management of shared memory resources is
crucial in delivering end-to-end QoS. Previous frameworks have either focused
on singular QoS targets or the allocation of partitionable resources among CPU
applications at relatively slow timescales. However, heterogeneous MPSoCs
typically require instant response from the memory system where most resources
cannot be partitioned. Moreover, the health of different cores in a
heterogeneous MPSoC is often measured by diverse performance objectives. In
this work, we propose a Self-Aware Resource Allocation (SARA) framework for
heterogeneous MPSoCs. Priority-based adaptation allows cores to use different
target performance and self-monitor their own intrinsic health. In response,
the system allocates non-partitionable resources based on priorities. The
proposed framework meets a diverse range of QoS demands from heterogeneous
cores.Comment: Accepted by the 55th annual Design Automation Conference 2018
(DAC'18
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