2,288 research outputs found
A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics
Mesoscopic simulations of hydrocarbon flow in source shales are challenging,
in part due to the heterogeneous shale pores with sizes ranging from a few
nanometers to a few micrometers. Additionally, the sub-continuum fluid-fluid
and fluid-solid interactions in nano- to micro-scale shale pores, which are
physically and chemically sophisticated, must be captured. To address those
challenges, we present a GPU-accelerated package for simulation of flow in
nano- to micro-pore networks with a many-body dissipative particle dynamics
(mDPD) mesoscale model. Based on a fully distributed parallel paradigm, the
code offloads all intensive workloads on GPUs. Other advancements, such as
smart particle packing and no-slip boundary condition in complex pore
geometries, are also implemented for the construction and the simulation of the
realistic shale pores from 3D nanometer-resolution stack images. Our code is
validated for accuracy and compared against the CPU counterpart for speedup. In
our benchmark tests, the code delivers nearly perfect strong scaling and weak
scaling (with up to 512 million particles) on up to 512 K20X GPUs on Oak Ridge
National Laboratory's (ORNL) Titan supercomputer. Moreover, a single-GPU
benchmark on ORNL's SummitDev and IBM's AC922 suggests that the host-to-device
NVLink can boost performance over PCIe by a remarkable 40\%. Lastly, we
demonstrate, through a flow simulation in realistic shale pores, that the CPU
counterpart requires 840 Power9 cores to rival the performance delivered by our
package with four V100 GPUs on ORNL's Summit architecture. This simulation
package enables quick-turnaround and high-throughput mesoscopic numerical
simulations for investigating complex flow phenomena in nano- to micro-porous
rocks with realistic pore geometries
Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI: Characterization, Designs, and Performance Evaluation
TensorFlow has been the most widely adopted Machine/Deep Learning framework.
However, little exists in the literature that provides a thorough understanding
of the capabilities which TensorFlow offers for the distributed training of
large ML/DL models that need computation and communication at scale. Most
commonly used distributed training approaches for TF can be categorized as
follows: 1) Google Remote Procedure Call (gRPC), 2) gRPC+X: X=(InfiniBand
Verbs, Message Passing Interface, and GPUDirect RDMA), and 3) No-gRPC: Baidu
Allreduce with MPI, Horovod with MPI, and Horovod with NVIDIA NCCL. In this
paper, we provide an in-depth performance characterization and analysis of
these distributed training approaches on various GPU clusters including the Piz
Daint system (6 on Top500). We perform experiments to gain novel insights along
the following vectors: 1) Application-level scalability of DNN training, 2)
Effect of Batch Size on scaling efficiency, 3) Impact of the MPI library used
for no-gRPC approaches, and 4) Type and size of DNN architectures. Based on
these experiments, we present two key insights: 1) Overall, No-gRPC designs
achieve better performance compared to gRPC-based approaches for most
configurations, and 2) The performance of No-gRPC is heavily influenced by the
gradient aggregation using Allreduce. Finally, we propose a truly CUDA-Aware
MPI Allreduce design that exploits CUDA kernels and pointer caching to perform
large reductions efficiently. Our proposed designs offer 5-17X better
performance than NCCL2 for small and medium messages, and reduces latency by
29% for large messages. The proposed optimizations help Horovod-MPI to achieve
approximately 90% scaling efficiency for ResNet-50 training on 64 GPUs.
Further, Horovod-MPI achieves 1.8X and 3.2X higher throughput than the native
gRPC method for ResNet-50 and MobileNet, respectively, on the Piz Daint
cluster.Comment: 10 pages, 9 figures, submitted to IEEE IPDPS 2019 for peer-revie
Analysis and evaluation of MapReduce solutions on an HPC cluster
This is a post-peer-review, pre-copyedit version of an article published in Computers & Electrical Engineering. The final authenticated version is available online at: https://doi.org/10.1016/j.compeleceng.2015.11.021[Abstract] The ever growing needs of Big Data applications are demanding challenging capabilities which cannot be handled easily by traditional systems, and thus more and more organizations are adopting High Performance Computing (HPC) to improve scalability and efficiency. Moreover, Big Data frameworks like Hadoop need to be adapted to leverage the available resources in HPC environments. This situation has caused the emergence of several HPC-oriented MapReduce frameworks, which benefit from different technologies traditionally oriented to supercomputing, such as high-performance interconnects or the message-passing interface. This work aims to establish a taxonomy of these frameworks together with a thorough evaluation, which has been carried out in terms of performance and energy efficiency metrics. Furthermore, the adaptability to emerging disks technologies, such as solid state drives, has been assessed. The results have shown that new frameworks like DataMPI can outperform Hadoop, although using IP over InfiniBand also provides significant benefits without code modifications.Ministerio de Economía y Competitividad; TIN2013-42148-
Evaluation of the PlayStation 2 as a cluster computing node
Cluster computing is currently a popular, cost-effective solution to the increasing computational demands of many applications in scientific computing and image processing. A cluster computer is comprised of several networked computers known as nodes. Since the goal of cluster computing is to provide a cost-effective means to processing computationally demanding applications, nodes that can be obtained at a low price with minimal performance tradeoff are always attractive. Presently, the most common cluster computers are comprised of networks of workstations constructed from commodity components. Recent trends have shown that computers being developed and deployed for purposes other than traditional personal computers or workstations have presented new candidates for cluster computing nodes. The new computing node candidates being considered may provide a competitive and even less expensive alternative to the cluster computing nodes being used today. Machines such as video game consoles, whose prices are kept extremely low due to intense marketplace competition, are a prime example of such machines. The Sony PlayStation 2, in particular, provides the user with low-level hardware devices that are often found in more expensive machines. This work presents and evaluation of the PlayStation 2 video game console as a cluster computing node for scientific and image processing applications. From this evaluation, a determination is made as to whether the PlayStation 2 is a viable alternative to the cluster computing nodes being used today
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