4,362 research outputs found
A Survey of Techniques for Improving Security of GPUs
Graphics processing unit (GPU), although a powerful performance-booster, also
has many security vulnerabilities. Due to these, the GPU can act as a
safe-haven for stealthy malware and the weakest `link' in the security `chain'.
In this paper, we present a survey of techniques for analyzing and improving
GPU security. We classify the works on key attributes to highlight their
similarities and differences. More than informing users and researchers about
GPU security techniques, this survey aims to increase their awareness about GPU
security vulnerabilities and potential countermeasures
CoreTSAR: Task Scheduling for Accelerator-aware Runtimes
Heterogeneous supercomputers that incorporate computational accelerators
such as GPUs are increasingly popular due to their high
peak performance, energy efficiency and comparatively low cost.
Unfortunately, the programming models and frameworks designed
to extract performance from all computational units still lack the
flexibility of their CPU-only counterparts. Accelerated OpenMP
improves this situation by supporting natural migration of OpenMP
code from CPUs to a GPU. However, these implementations currently
lose one of OpenMP’s best features, its flexibility: typical
OpenMP applications can run on any number of CPUs. GPU implementations
do not transparently employ multiple GPUs on a node
or a mix of GPUs and CPUs. To address these shortcomings, we
present CoreTSAR, our runtime library for dynamically scheduling
tasks across heterogeneous resources, and propose straightforward
extensions that incorporate this functionality into Accelerated
OpenMP. We show that our approach can provide nearly linear
speedup to four GPUs over only using CPUs or one GPU while
increasing the overall flexibility of Accelerated OpenMP
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
Pervasive Parallel And Distributed Computing In A Liberal Arts College Curriculum
We present a model for incorporating parallel and distributed computing (PDC) throughout an undergraduate CS curriculum. Our curriculum is designed to introduce students early to parallel and distributed computing topics and to expose students to these topics repeatedly in the context of a wide variety of CS courses. The key to our approach is the development of a required intermediate-level course that serves as a introduction to computer systems and parallel computing. It serves as a requirement for every CS major and minor and is a prerequisite to upper-level courses that expand on parallel and distributed computing topics in different contexts. With the addition of this new course, we are able to easily make room in upper-level courses to add and expand parallel and distributed computing topics. The goal of our curricular design is to ensure that every graduating CS major has exposure to parallel and distributed computing, with both a breadth and depth of coverage. Our curriculum is particularly designed for the constraints of a small liberal arts college, however, much of its ideas and its design are applicable to any undergraduate CS curriculum
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