We propose a novel compositing pipeline and a dynamic load balancing technique for volume rendering which utilizes a two-layered group structure to achieve effective and scalable load balancing. The technique enables each process to render data from non-contiguous regions of the volume with minimal impact on the total render time. We demonstrate the effectiveness of the proposed technique by performing a set of experiments on a modern GPU cluster. The experiments show that using the technique results in up to a 35.7% lower worst-case memory usage as compared to a dynamic k-d tree load balancing technique, whilst simultaneously achieving similar or higher render performance. The proposed technique was also able to lower the amount of transferred data during the load balancing stage by up to 72.2%. The technique has the potential to be used in many scenarios where other dynamic load balancing techniques have proved to be inadequate, such as during large-scale visualization. key words: large-scale visualization, distributed computing, load balancing, GPU

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Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

FreeSurfer is among the most widely used suites of software for the study of cortical and subcortical brain anatomy. However, analysis using FreeSurfer can be time-consuming and it lacks support for the graphics processing units (GPUs) after the core development team stopped maintaining GPU-accelerated versions due to significant programming cost. As FreeSurfer is a large project with millions of source lines, in this work, we introduce and examine the use of a directive-based framework, OpenACC, in GPU acceleration of FreeSurfer, and we found the OpenACC-based approach significantly reduces programming costs. Moreover, because the overhead incurred by CPU-to-GPU data transfer is the major challenge in delivering GPU-based codes of high performance, we compare two schemes, copy-and-transfer and overlapped-fully-transfer, to reduce such data transfer overhead. Experimental results show that the target function we accelerated with overlapped-fully-transfer scheme ran 2.3x as fast as the original CPU-based function, and the GPU-accelerated program achieved an average speedup of 1.2x compared to the original CPU-based program. These results demonstrate the usefulness and potential of utilizing the proposed OpenACC-based approach to integrate GPU support for FreeSurfer which can be easily extended to other computationally expensive functions and modules of FreeSurfer to achieve further speedup.