1,903 research outputs found
TensorFlow Doing HPC
TensorFlow is a popular emerging open-source programming framework supporting
the execution of distributed applications on heterogeneous hardware. While
TensorFlow has been initially designed for developing Machine Learning (ML)
applications, in fact TensorFlow aims at supporting the development of a much
broader range of application kinds that are outside the ML domain and can
possibly include HPC applications. However, very few experiments have been
conducted to evaluate TensorFlow performance when running HPC workloads on
supercomputers. This work addresses this lack by designing four traditional HPC
benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG)
solver and Fast Fourier Transform (FFT). We analyze their performance on two
supercomputers with accelerators and evaluate the potential of TensorFlow for
developing HPC applications. Our tests show that TensorFlow can fully take
advantage of high performance networks and accelerators on supercomputers.
Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical
communication bandwidth on our testing platform. We find an approximately 2x,
1.7x and 1.8x performance improvement when increasing the number of GPUs from
two to four in the matrix-matrix multiply, CG and FFT applications
respectively. All our performance results demonstrate that TensorFlow has high
potential of emerging also as HPC programming framework for heterogeneous
supercomputers.Comment: Accepted for publication at The Ninth International Workshop on
Accelerators and Hybrid Exascale Systems (AsHES'19
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring
Compressive sensing promises to enable bandwidth-efficient on-board
compression of astronomical data by lifting the encoding complexity from the
source to the receiver. The signal is recovered off-line, exploiting GPUs
parallel computation capabilities to speedup the reconstruction process.
However, inherent GPU hardware constraints limit the size of the recoverable
signal and the speedup practically achievable. In this work, we design parallel
algorithms that exploit the properties of circulant matrices for efficient
GPU-accelerated sparse signals recovery. Our approach reduces the memory
requirements, allowing us to recover very large signals with limited memory. In
addition, it achieves a tenfold signal recovery speedup thanks to ad-hoc
parallelization of matrix-vector multiplications and matrix inversions.
Finally, we practically demonstrate our algorithms in a typical application of
circulant matrices: deblurring a sparse astronomical image in the compressed
domain
Distributed Training Large-Scale Deep Architectures
Scale of data and scale of computation infrastructures together enable the
current deep learning renaissance. However, training large-scale deep
architectures demands both algorithmic improvement and careful system
configuration. In this paper, we focus on employing the system approach to
speed up large-scale training. Via lessons learned from our routine
benchmarking effort, we first identify bottlenecks and overheads that hinter
data parallelism. We then devise guidelines that help practitioners to
configure an effective system and fine-tune parameters to achieve desired
speedup. Specifically, we develop a procedure for setting minibatch size and
choosing computation algorithms. We also derive lemmas for determining the
quantity of key components such as the number of GPUs and parameter servers.
Experiments and examples show that these guidelines help effectively speed up
large-scale deep learning training
The ESCAPE project : Energy-efficient Scalable Algorithms for Weather Prediction at Exascale
In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure.
The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors.
This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) and ALADIN (Aire Limitee Adaptation Dynamique Developpement International); and COSMO-EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf.
The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU-GPU arrangements
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