Graphics Processing Unit Framework Research Articles

Study on the application of artificial neural network-based flamelet/progress variable model in supersonic combustion

The flamelet model has the characteristics of high efficiency and physical intuition and has excellent application prospects in supersonic turbulent combustion simulation. Expanding the dimensions of the flamelet model is a potential direction for model development in order to improve its applicability and accuracy, but the accompanying surge in memory is a problem that must be avoided. Therefore, the idea of using the artificial neural network (ANN) model to replace the flamelet database is a feasible exploration currently and has been preliminarily applied in 2D flamelet databases based on central processing unit frameworks. Based on the 3D flamelet database of the flamelet/progress variable (FPV) model, this article studies the strategy of using ANN to replace the flamelet database of the FPV model in a graphics processing unit framework. Due to the significant influence of the progress variable source term and heat release rate on the combustion calculation and the large range of these two parameters, four data processing methods are used to train the parameters separately, and three indicators are used to evaluate the training performance. Subsequently, based on the ANN model using different data processing methods mentioned earlier, calculations are conducted on a hydrogen-fueled supersonic combustion, and the computational accuracy is evaluated. The results indicate that the strategy proposed in this study can screen out artificial neural network replacement models with the same accuracy as the traditional flamelet model.

High-performance attribute reduction on graphics processing unit

ABSTRACT Recently, graphics processing unit (GPU) gained lots of attention from academia and industry for its applicability in high-performance computing. It has been successfully applied to many fields, such as image processing, machine learning, object detection, etc. In our previous work, GPU was adopted to accelerate the computation of rough set approximation (RSA), which is the core step in most of the rough sets based tasks, e.g. attribute reduction. The method is essentially a CPU-GPU cooperative paradigm. That is to say, there are lots of data exchanged between host memory and GPU memory, which greatly degrades the performance of the system. This paper introduces a unified GPU framework for parallel attribute reduction, in which two critical steps in attribute reduction, i.e. computation of equivalence class and attributes significance, are both executed on GPU. Moreover, the algorithm is well designed by exploiting the architectural characteristics of the modern GPU architecture. Experiments were carried out on data sets with different sizes. The results show that the proposed algorithm can outperform the CPU-GPU cooperative algorithm on large data sets.

A GPU-based numerical manifold method for modeling the formation of the excavation damaged zone in deep rock tunnels

In this study, combined with the zero–thickness cohesive element (ZE) model and explicit integration method, a parallelization technique based on graphics processing units (GPU) is proposed to accelerate the computational efficiency of the NMM for modeling the formation of the excavation damaged zone in deep rock tunnels. To optimize the performance of the original NMM when simulating rock masses with fine grains, a ZE model is adopted and a speedup ratio of 41 compared with the original NMM is achieved. To simulate the behavior of the failed rock masses more efficiently and to achieve a higher speedup ratio after parallelization, the explicit integration scheme is introduced to avoid the assembly and solving of linear equations. To further improve the computation efficiency of hardware–based approaches, the GPU–based parallel technique, including a hybrid CPU–GPU framework, a ‘single instruction on multiple data’ (SIMD) model and a rainbow coloring strategy, is adopted and a speedup ratio of up to 34.7x is achieved. Finally, a series of excavation models are simulated, and the predicted results validate the efficiency and capability of this developed method for modeling the formation of the excavation damaged zone in deeply buried rock tunnels.

An efficient GPU algorithm for tetrahedron-based Brillouin-zone integration

We report an efficient algorithm for calculating momentum-space integrals in solid state systems on modern graphics processing units (GPUs). Our algorithm is based on the tetrahedron method, which we demonstrate to be ideally suited for execution in a GPU framework. In order to achieve maximum performance, all floating point operations are executed in single precision. For benchmarking our implementation within the CUDA programming framework we calculate the orbital-resolved density of states in an iron-based superconductor. However, our algorithm is general enough for the achieved improvements to carry over to the calculation of other momentum integrals such as, e.g. susceptibilities. If our program code is integrated into an existing program for the central processing unit (CPU), i.e. when data transfer overheads exist, speedups of up to a factor ∼130 compared to a pure CPU implementation can be achieved, largely depending on the problem size. In case our program code is integrated into an existing GPU program, speedups over a CPU implementation of up to a factor ∼165 are possible, even for moderately sized workloads.

MASK

Graphics Processing Units (GPUs) exploit large amounts of threadlevel parallelism to provide high instruction throughput and to efficiently hide long-latency stalls. The resulting high throughput, along with continued programmability improvements, have made GPUs an essential computational resource in many domains. Applications from different domains can have vastly different compute and memory demands on the GPU. In a large-scale computing environment, to efficiently accommodate such wide-ranging demands without leaving GPU resources underutilized, multiple applications can share a single GPU, akin to how multiple applications execute concurrently on a CPU. Multi-application concurrency requires several support mechanisms in both hardware and software. One such key mechanism is virtual memory, which manages and protects the address space of each application. However, modern GPUs lack the extensive support for multi-application concurrency available in CPUs, and as a result suffer from high performance overheads when shared by multiple applications, as we demonstrate. We perform a detailed analysis of which multi-application concurrency support limitations hurt GPU performance the most. We find that the poor performance is largely a result of the virtual memory mechanisms employed in modern GPUs. In particular, poor address translation performance is a key obstacle to efficient GPU sharing. State-of-the-art address translation mechanisms, which were designed for single-application execution, experience significant inter-application interference when multiple applications spatially share the GPU. This contention leads to frequent misses in the shared translation lookaside buffer (TLB), where a single miss can induce long-latency stalls for hundreds of threads. As a result, the GPU often cannot schedule enough threads to successfully hide the stalls, which diminishes system throughput and becomes a first-order performance concern. Based on our analysis, we propose MASK, a new GPU framework that provides low-overhead virtual memory support for the concurrent execution of multiple applications. MASK consists of three novel address-translation-aware cache and memory management mechanisms that work together to largely reduce the overhead of address translation: (1) a token-based technique to reduce TLB contention, (2) a bypassing mechanism to improve the effectiveness of cached address translations, and (3) an application-aware memory scheduling scheme to reduce the interference between address translation and data requests. Our evaluations show that MASK restores much of the throughput lost to TLB contention. Relative to a state-of-the-art GPU TLB, MASK improves system throughput by 57.8%, improves IPC throughput by 43.4%, and reduces applicationlevel unfairness by 22.4%. MASK's system throughput is within 23.2% of an ideal GPU system with no address translation overhead.

GPU accelerated simplified harmonic spherical approximation equations for three-dimensional optical imaging

Simplified spherical harmonics approximation (SPN) equations are widely used in modeling light propagation in biological tissues. However, with the increase of order N, its computational burden will severely aggravate. We propose a graphics processing unit (GPU) accelerated framework for SPN equations. Compared with the conventional central processing unit implementation, an increased performance of the GPU framework is obtained with an increase in mesh size, with the best speed-up ratio of 25 among the studied cases. The influence of thread distribution on the performance of the GPU framework is also investigated.

Elastography using multi-stream GPU: an application to online tracked ultrasound elastography, in-vivo and the da Vinci Surgical System.

A system for real-time ultrasound (US) elastography will advance interventions for the diagnosis and treatment of cancer by advancing methods such as thermal monitoring of tissue ablation. A multi-stream graphics processing unit (GPU) based accelerated normalized cross-correlation (NCC) elastography, with a maximum frame rate of 78 frames per second, is presented in this paper. A study of NCC window size is undertaken to determine the effect on frame rate and the quality of output elastography images. This paper also presents a novel system for Online Tracked Ultrasound Elastography (O-TRuE), which extends prior work on an offline method. By tracking the US probe with an electromagnetic (EM) tracker, the system selects in-plane radio frequency (RF) data frames for generating high quality elastograms. A novel method for evaluating the quality of an elastography output stream is presented, suggesting that O-TRuE generates more stable elastograms than generated by untracked, free-hand palpation. Since EM tracking cannot be used in all systems, an integration of real-time elastography and the da Vinci Surgical System is presented and evaluated for elastography stream quality based on our metric. The da Vinci surgical robot is outfitted with a laparoscopic US probe, and palpation motions are autonomously generated by customized software. It is found that a stable output stream can be achieved, which is affected by both the frequency and amplitude of palpation. The GPU framework is validated using data from in-vivo pig liver ablation; the generated elastography images identify the ablated region, outlined more clearly than in the corresponding B-mode US images.

A CPU–GPU framework for optimizing the quality of large meshes

The automatic generation of 3D finite element meshes (FEM) is still a bottleneck for the simulation of large fluid dynamic problems. Although today there are several algorithms that can generate good meshes without user intervention, in cases where the geometry changes during the calculation and thousands of meshes must be constructed, the computational cost of this process can exceed the cost of the FEM. There has been a lot of work in FEM parallelization and the algorithms work well in different parallel architectures, but at present there has not been much success in the parallelization of mesh generation methods. This paper will present a massive parallelization scheme for re-meshing with tetrahedral elements using the local modification algorithm. This method is frequently used to improve the quality of elements once the mesh has been generated, but we will show it can also be applied as a regeneration process, starting with the distorted and invalid mesh of the previous step. The parallelization is carried out using OpenCL and OpenMP in order to test the method in a multiple CPU architecture and also in Graphics Processing Units (GPUs). Finally we present the speedup and quality results obtained in meshes with hundreds of thousands of elements and different parallel APIs.

Monte Carlo‐based flood modelling framework for estimating probability weighted flood risk

AbstractThis study presents a new Monte Carlo‐based flood inundation modelling framework for estimating probability weighted flood risk using a computationally efficient graphics processing unit (GPU) two dimensional (2D) hydraulic model. The 2D flood model is programmed in the GPU framework providing a unique ability to run numerous simulations in a short period of time, permitting the integration of 2D hydraulic modelling into Monte Carlo analysis. The framework operates by performing many 2D flood simulations of randomly sampled input parameters to develop a spatially varied flood hazard map. The probabilistic framework is demonstrated using a 1% annual probability flood event and simulating 1000 different flood simulations by randomly selected peak flows of the Swannanoa River in Buncombe County, USA. The results, in general, display benefits of probabilistic flood risk approach compared with a single simulation approach. The latter approach underestimated 28% of flood risk relative to the former. As the number of simulations increased from 1 to 1000, areas identified as low danger and judgment zone increased by 87.4% and 36.8% respectively, whereas the high danger zone increased by 9.3%. In conclusion, the new Monte Carlo flood risk modelling framework has the ability to provide improved accuracy of flood risk and greater insight into the spatial distribution of flood risk.