We developed a high-speed image reduction pipeline using Graphics Processing Units (GPUs) as hardware accelerators. Astronomers desire detecting EM counterpart of gravitational-wave sources as soon as possible for sharing positional information to organize systematic follow-up observations. Therefore, high-speed image processing is important. We developed a new image reduction pipeline for our robotic telescope system, which uses a GPU via a Python package CuPy to achieve high-speed image processing. As a result, the processing speed was increased by more than a factor of forty to that of the current pipeline, while maintaining the same functions.
In this paper a new scalable hydrodynamic code GPUPEGAS (GPU-accelerated PErformance Gas Astrophysic Simulation) for simulation of interacting galaxies is proposed. The code is based on combination of Godunov method as well as on the original implementation of FlIC method, specially adapted for GPU-implementation. Fast Fourier Transform is used for Poisson equation solution in GPUPEGAS. Software implementation of the above methods was tested on classical gas dynamics problems, new Aksenov's test and classical gravitational gas dynamics problems. Collisionless hydrodynamic approach was used for…
We present a GPU-accelerated cosmological simulation code, PhotoNs-GPU, based on algorithm of Particle Mesh Fast Multipole Method (PM-FMM), and focus on the GPU utilization and optimization. A proper interpolated method for truncated gravity is introduced to speed up the special functions in kernels. We verify the GPU code in mixed precision and different levels of interpolated method on GPU. A run with single precision is roughly two times faster that double precision for current practical cosmological simulations. But it could induce a unbiased small noise in power spectrum. Comparing with t…
The conservative Post-Newtonian (PN) Hamiltonian formulation of spinning compact binaries has six integrals of motion including the total energy, the total angular momentum and the constant unit lengths of spins. The manifold correction method can effectively eliminate the integration errors accumulation in a long time. In this paper, the accelerated manifold correction method based on graphics processing unit (GPU) is designed to simulate the dynamic evolution of spinning compact binaries. The feasibility and the efficiency of parallel computation on GPU for spinning compact binaries have bee…
We present a GPU-accelerated numerical approach for fast kernel and differential background solutions. The model image proposed in the Bramich (2008) difference image analysis algorithm is analogous to a very simple Convolutional Neural Network (CNN), with a single convolutional filter (i.e. the kernel) and an added scalar bias (i.e. the differential background). Here, we do not solve for the discrete pixel array in the classical, analytical linear least-squares sense. Instead, by making use of PyTorch tensors (GPU compatible multi-dimensional matrices) and associated deep learning tools, we s…
Searching for sources of electromagnetic emission in spectral-line radio astronomy interferometric data is a computationally intensive process. Parallel programming techniques and High Performance Computing hardware may be used to improve the computational performance of a source finding program. However, it is desirable to further reduce the processing time of source finding in order to decrease the computational resources required for the task. GPU acceleration is a method that may achieve significant increases in performance for some source finding algorithms, particularly for filtering ima…
We describe a parallel hybrid symplectic integrator for planetary system integration that runs on a graphics processing unit (GPU). The integrator identifies close approaches between particles and switches from symplectic to Hermite algorithms for particles that require higher resolution integrations. The integrator is approximately as accurate as other hybrid symplectic integrators but is GPU accelerated.
We have developed a flexible radio-frequency readout system suitable for a variety of superconducting detectors commonly used in millimeter and submillimeter astrophysics, including Kinetic Inductance detectors (KIDs), Thermal KID bolometers (TKIDs), and Quantum Capacitance Detectors (QCDs). Our system avoids custom FPGA-based readouts and instead uses commercially available software radio hardware for ADC/DAC and a GPU to handle real time signal processing. Because this system is written in common C++/CUDA, the range of different algorithms that can be quickly implemented make it suitable for…
Mercury-Opal: the GPU-accelerated version of the n-body code for planet formation Mercury-Arxes
INAF - Osservatorio Astronomico di Trieste（意大利国家天体物理研究所的里雅斯特天文台）；ICSC National Centre for HPC, Big Data and Quantum Computing（高性能计算、大数据和量子计算国家中心）；INAF - Osservatorio Astrofisico di Torino（意大利国家天体物理研究所都灵天文台）；INAF - Istituto di Astrofisica e Planetologia Spaziali（意大利国家天体物理研究所天文与行星星系物理学研究所）；University（大学）
We describe a computational framework for hierarchical Bayesian inference with simple (typically single-plate) parametric graphical models that uses graphics processing units (GPUs) to accelerate computations, enabling deployment on very large datasets. Its C++ implementation, CUDAHM (CUDA for Hierarchical Models) exploits conditional independence between instances of a plate, facilitating massively parallel exploration of the replication parameter space using the single instruction, multiple data architecture of GPUs. It provides support for constructing Metropolis-within-Gibbs samplers that …
We present the newly developed code, GAMER (GPU-accelerated Adaptive MEsh Refinement code), which has adopted a novel approach to improve the performance of adaptive mesh refinement (AMR) astrophysical simulations by a large factor with the use of the graphic processing unit (GPU). The AMR implementation is based on a hierarchy of grid patches with an oct-tree data structure. We adopt a three-dimensional relaxing TVD scheme for the hydrodynamic solver, and a multi-level relaxation scheme for the Poisson solver. Both solvers have been implemented in GPU, by which hundreds of patches can be adva…
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 req…
We present the smoothed-particle hydrodynamics simulation code, Bonsai-SPH, which is a continuation of our previously developed gravity-only hierarchical $N$-body code (called Bonsai). The code is optimized for Graphics Processing Unit (GPU) accelerators which enables researchers to take advantage of these powerful computational resources. Bonsa-SPH produces simulation results comparable with state-of-the-art, CPU based, codes, but using an order of magnitude less computation time. The code is freely available online and the details are described in this work.
We have recently carried out a computational campaign to investigate a model of coronal heating in three-dimensions using reduced magnetohydrodynamics (RMHD). Our code is built on a conventional scheme using the pseudo-spectral method, and is parallelized using MPI. The current investigation requires very long time integrations using high Lundquist numbers, where the formation of very fine current layers challenge the resolutions achievable even on massively parallel machines. We present here results of a port to Nvidia CUDA (Compute Unified Device Architecture) for hardware acceleration using…
We present a GPU accelerated CUDA-C implementation of the Barnes Hut (BH) tree code for calculating the gravitational potential on octree adaptive meshes. The tree code algorithm is implemented within the FLASH4 adaptive mesh refinement (AMR) code framework and therefore fully MPI parallel. We describe the algorithm and present test results that demonstrate its accuracy and performance in comparison to the algorithms available in the current FLASH4 version. We use a MacLaurin spheroid to test the accuracy of our new implementation and use spherical, collapsing cloud cores with effective AMR to…
This paper presents, to the author's knowledge, the first graphics processing unit (GPU) accelerated program that solves the evolution of interacting scalar fields in an expanding universe. We present the implementation in NVIDIA's Compute Unified Device Architecture (CUDA) and compare the performance to other similar programs in chaotic inflation models. We report speedups between one and two orders of magnitude depending on the used hardware and software while achieving small errors in single precision. Simulations that used to last roughly one day to compute can now be done in hours and thi…
