74,124 research outputs found
Vivaldi: a Python-like domain-specific language for volume rendering and processing on distributed multi CPU-GPU systems
Department of Computer EngineeringIn this thesis, a Python-like domain-specific language alled Vivaldi is proposed. Vivaldi is based on Python, but can also provide parallel volume rendering and processing on distributed heterogeneous systems. Nowadays, visualization requires high performance for processing large data and creating high quality images. Therefore, computing systems have also advanced to meet this requirement; one example is graphics processing unit (GPU) clusters. However, even though high-performance systems exist, effective utilization of these systems is not easy for non-experts such as domain scientists and researchers because they require expert programming skills and a significant amount of software development for parallel programming, distributed systems, and heterogeneous architecture.
One aim of Vivaldi is to minimize these required programming skills using a domain-specific language
for volume rendering and visualization that is Python-like and platform independent. In this language, a parallel visualization model, virtual shared memory model, and platform independent architecture for distributed heterogeneous systems are proposed. The parallel visualization model provides a simple means to implement visualization pipelines. The virtual shared memory model enables the use of cluster memory without Message Passing Interface (MPI) and CUDA. Finally, the platform independent design integrates central processing unit(CPU)s and GPUs into a common, domain-specific language. Vivaldi code was compared to C++ implementations to evaluate its performance according to number of lines, performance, and scalability. The results show that Vivaldi achieved comparable scalability and performance while
requiring much less programming effort.ope
Unleashing the Power of Distributed CPU/GPU Architectures: Massive Astronomical Data Analysis and Visualization case study
Upcoming and future astronomy research facilities will systematically
generate terabyte-sized data sets moving astronomy into the Petascale data era.
While such facilities will provide astronomers with unprecedented levels of
accuracy and coverage, the increases in dataset size and dimensionality will
pose serious computational challenges for many current astronomy data analysis
and visualization tools. With such data sizes, even simple data analysis tasks
(e.g. calculating a histogram or computing data minimum/maximum) may not be
achievable without access to a supercomputing facility.
To effectively handle such dataset sizes, which exceed today's single machine
memory and processing limits, we present a framework that exploits the
distributed power of GPUs and many-core CPUs, with a goal of providing data
analysis and visualizing tasks as a service for astronomers. By mixing shared
and distributed memory architectures, our framework effectively utilizes the
underlying hardware infrastructure handling both batched and real-time data
analysis and visualization tasks. Offering such functionality as a service in a
"software as a service" manner will reduce the total cost of ownership, provide
an easy to use tool to the wider astronomical community, and enable a more
optimized utilization of the underlying hardware infrastructure.Comment: 4 Pages, 1 figures, To appear in the proceedings of ADASS XXI, ed.
P.Ballester and D.Egret, ASP Conf. Serie
GPU Accelerated Particle Visualization with Splotch
Splotch is a rendering algorithm for exploration and visual discovery in
particle-based datasets coming from astronomical observations or numerical
simulations. The strengths of the approach are production of high quality
imagery and support for very large-scale datasets through an effective mix of
the OpenMP and MPI parallel programming paradigms. This article reports our
experiences in re-designing Splotch for exploiting emerging HPC architectures
nowadays increasingly populated with GPUs. A performance model is introduced
for data transfers, computations and memory access, to guide our re-factoring
of Splotch. A number of parallelization issues are discussed, in particular
relating to race conditions and workload balancing, towards achieving optimal
performances. Our implementation was accomplished by using the CUDA programming
paradigm. Our strategy is founded on novel schemes achieving optimized data
organisation and classification of particles. We deploy a reference simulation
to present performance results on acceleration gains and scalability. We
finally outline our vision for future work developments including possibilities
for further optimisations and exploitation of emerging technologies.Comment: 25 pages, 9 figures. Astronomy and Computing (2014
DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives
We present a new parallel algorithm for probabilistic graphical model
optimization. The algorithm relies on data-parallel primitives (DPPs), which
provide portable performance over hardware architecture. We evaluate results on
CPUs and GPUs for an image segmentation problem. Compared to a serial baseline,
we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare
our performance to a reference, OpenMP-based algorithm, and find speedups of up
to 7X (CPU).Comment: LDAV 2018, October 201
Interactive Visualization of the Largest Radioastronomy Cubes
3D visualization is an important data analysis and knowledge discovery tool,
however, interactive visualization of large 3D astronomical datasets poses a
challenge for many existing data visualization packages. We present a solution
to interactively visualize larger-than-memory 3D astronomical data cubes by
utilizing a heterogeneous cluster of CPUs and GPUs. The system partitions the
data volume into smaller sub-volumes that are distributed over the rendering
workstations. A GPU-based ray casting volume rendering is performed to generate
images for each sub-volume, which are composited to generate the whole volume
output, and returned to the user. Datasets including the HI Parkes All Sky
Survey (HIPASS - 12 GB) southern sky and the Galactic All Sky Survey (GASS - 26
GB) data cubes were used to demonstrate our framework's performance. The
framework can render the GASS data cube with a maximum render time < 0.3 second
with 1024 x 1024 pixels output resolution using 3 rendering workstations and 8
GPUs. Our framework will scale to visualize larger datasets, even of Terabyte
order, if proper hardware infrastructure is available.Comment: 15 pages, 12 figures, Accepted New Astronomy July 201
Volume visualization of time-varying data using parallel, multiresolution and adaptive-resolution techniques
This paper presents a parallel rendering approach that allows high-quality visualization of large time-varying volume datasets. Multiresolution and adaptive-resolution techniques are also incorporated to improve the efficiency of the rendering. Three basic steps are needed to implement this kind of an application. First we divide the task through decomposition of data. This decomposition can be either temporal or spatial or a mix of both. After data has been divided, each of the data portions is rendered by a separate processor to create sub-images or frames. Finally these sub-images or frames are assembled together into a final image or animation. After developing this application, several experiments were performed to show that this approach indeed saves time when a reasonable number of processors are used. Also, we conclude that the optimal number of processors is dependent on the size of the dataset used
- …