956 research outputs found
Petascale turbulence simulation using a highly parallel fast multipole method on GPUs
This paper reports large-scale direct numerical simulations of
homogeneous-isotropic fluid turbulence, achieving sustained performance of 1.08
petaflop/s on gpu hardware using single precision. The simulations use a vortex
particle method to solve the Navier-Stokes equations, with a highly parallel
fast multipole method (FMM) as numerical engine, and match the current record
in mesh size for this application, a cube of 4096^3 computational points solved
with a spectral method. The standard numerical approach used in this field is
the pseudo-spectral method, relying on the FFT algorithm as numerical engine.
The particle-based simulations presented in this paper quantitatively match the
kinetic energy spectrum obtained with a pseudo-spectral method, using a trusted
code. In terms of parallel performance, weak scaling results show the fmm-based
vortex method achieving 74% parallel efficiency on 4096 processes (one gpu per
mpi process, 3 gpus per node of the TSUBAME-2.0 system). The FFT-based spectral
method is able to achieve just 14% parallel efficiency on the same number of
mpi processes (using only cpu cores), due to the all-to-all communication
pattern of the FFT algorithm. The calculation time for one time step was 108
seconds for the vortex method and 154 seconds for the spectral method, under
these conditions. Computing with 69 billion particles, this work exceeds by an
order of magnitude the largest vortex method calculations to date
Topology-aware GPU scheduling for learning workloads in cloud environments
Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud, are enabling deep learning in various domains including health care, autonomous vehicles, and Internet of Things. Multi-GPU systems exhibit complex connectivity among GPUs and between GPUs and CPUs. Workload schedulers must consider hardware topology and workload communication requirements in order to allocate CPU and GPU resources for optimal execution time and improved utilization in shared cloud environments.
This paper presents a new topology-aware workload placement strategy to schedule deep learning jobs on multi-GPU systems. The placement strategy is evaluated with a prototype on a Power8 machine with Tesla P100 cards, showing speedups of up to ≈1.30x compared to state-of-the-art strategies; the proposed algorithm achieves this result by allocating GPUs that satisfy workload requirements while preventing interference. Additionally, a large-scale simulation shows that the proposed strategy provides higher resource utilization and performance in cloud systems.This project is supported by the IBM/BSC Technology Center for Supercomputing
collaboration agreement. It has also received funding from the European Research Council (ERC) under the European Union’s Horizon
2020 research and innovation programme (grant agreement No 639595). It is
also partially supported by the Ministry of Economy of Spain under contract
TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051,
by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program
(SEV-2015-0493). We thank our IBM Research colleagues Alaa Youssef
and Asser Tantawi for the valuable discussions. We also thank SC17 committee
member Blair Bethwaite of Monash University for his constructive feedback on the earlier drafts of this paper.Peer ReviewedPostprint (published version
Predicting Intermediate Storage Performance for Workflow Applications
Configuring a storage system to better serve an application is a challenging
task complicated by a multidimensional, discrete configuration space and the
high cost of space exploration (e.g., by running the application with different
storage configurations). To enable selecting the best configuration in a
reasonable time, we design an end-to-end performance prediction mechanism that
estimates the turn-around time of an application using storage system under a
given configuration. This approach focuses on a generic object-based storage
system design, supports exploring the impact of optimizations targeting
workflow applications (e.g., various data placement schemes) in addition to
other, more traditional, configuration knobs (e.g., stripe size or replication
level), and models the system operation at data-chunk and control message
level.
This paper presents our experience to date with designing and using this
prediction mechanism. We evaluate this mechanism using micro- as well as
synthetic benchmarks mimicking real workflow applications, and a real
application.. A preliminary evaluation shows that we are on a good track to
meet our objectives: it can scale to model a workflow application run on an
entire cluster while offering an over 200x speedup factor (normalized by
resource) compared to running the actual application, and can achieve, in the
limited number of scenarios we study, a prediction accuracy that enables
identifying the best storage system configuration
Heterogeneity- and homophily-induced vulnerability of a P2P network formation model: the IOTA auto-peering protocol
IOTA is a distributed ledger technology that relies on a peer-to-peer (P2P)
network for communications. Recently an auto-peering algorithm was proposed to
build connections among IOTA peers according to their "Mana" endowment, which
is an IOTA internal reputation system. This paper's goal is to detect potential
vulnerabilities and evaluate the resilience of the P2P network generated using
IOTA auto-peering algorithm against eclipse attacks. In order to do so, we
interpret IOTA's auto-peering algorithm as a random network formation model and
employ different network metrics to identify cost-efficient partitions of the
network. As a result, we present a potential strategy that an attacker can use
to eclipse a significant part of the network, providing estimates of costs and
potential damage caused by the attack. On the side, we provide an analysis of
the properties of IOTA auto-peering network ensemble, as an interesting class
of homophile random networks in between 1D lattices and regular Poisson graphs
Collocation Games and Their Application to Distributed Resource Management
We introduce Collocation Games as the basis of a general framework for modeling, analyzing, and facilitating the interactions between the various stakeholders in distributed systems in general, and in cloud computing environments in particular. Cloud computing enables fixed-capacity (processing, communication, and storage) resources to be offered by infrastructure providers as commodities for sale at a fixed cost in an open marketplace to independent, rational parties (players) interested in setting up their own applications over the Internet. Virtualization technologies enable the partitioning of such fixed-capacity resources so as to allow each player to dynamically acquire appropriate fractions of the resources for unencumbered use. In such a paradigm, the resource management problem reduces to that of partitioning the entire set of applications (players) into subsets, each of which is assigned to fixed-capacity cloud resources. If the infrastructure and the various applications are under a single administrative domain, this partitioning reduces to an optimization problem whose objective is to minimize the overall deployment cost. In a marketplace, in which the infrastructure provider is interested in maximizing its own profit, and in which each player is interested in minimizing its own cost, it should be evident that a global optimization is precisely the wrong framework. Rather, in this paper we use a game-theoretic framework in which the assignment of players to fixed-capacity resources is the outcome of a strategic "Collocation Game". Although we show that determining the existence of an equilibrium for collocation games in general is NP-hard, we present a number of simplified, practically-motivated variants of the collocation game for which we establish convergence to a Nash Equilibrium, and for which we derive convergence and price of anarchy bounds. In addition to these analytical results, we present an experimental evaluation of implementations of some of these variants for cloud infrastructures consisting of a collection of multidimensional resources of homogeneous or heterogeneous capacities. Experimental results using trace-driven simulations and synthetically generated datasets corroborate our analytical results and also illustrate how collocation games offer a feasible distributed resource management alternative for autonomic/self-organizing systems, in which the adoption of a global optimization approach (centralized or distributed) would be neither practical nor justifiable.NSF (CCF-0820138, CSR-0720604, EFRI-0735974, CNS-0524477, CNS-052016, CCR-0635102); Universidad Pontificia Bolivariana; COLCIENCIAS–Instituto Colombiano para el Desarrollo de la Ciencia y la TecnologÃa "Francisco José de Caldas
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