6,953 research outputs found
Time-Shared Execution of Realtime Computer Vision Pipelines by Dynamic Partial Reconfiguration
This paper presents an FPGA runtime framework that demonstrates the
feasibility of using dynamic partial reconfiguration (DPR) for time-sharing an
FPGA by multiple realtime computer vision pipelines. The presented time-sharing
runtime framework manages an FPGA fabric that can be round-robin time-shared by
different pipelines at the time scale of individual frames. In this new
use-case, the challenge is to achieve useful performance despite high
reconfiguration time. The paper describes the basic runtime support as well as
four optimizations necessary to achieve realtime performance given the
limitations of DPR on today's FPGAs. The paper provides a characterization of a
working runtime framework prototype on a Xilinx ZC706 development board. The
paper also reports the performance of realtime computer vision pipelines when
time-shared
Gunrock: GPU Graph Analytics
For large-scale graph analytics on the GPU, the irregularity of data access
and control flow, and the complexity of programming GPUs, have presented two
significant challenges to developing a programmable high-performance graph
library. "Gunrock", our graph-processing system designed specifically for the
GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on
operations on a vertex or edge frontier. Gunrock achieves a balance between
performance and expressiveness by coupling high performance GPU computing
primitives and optimization strategies with a high-level programming model that
allows programmers to quickly develop new graph primitives with small code size
and minimal GPU programming knowledge. We characterize the performance of
various optimization strategies and evaluate Gunrock's overall performance on
different GPU architectures on a wide range of graph primitives that span from
traversal-based algorithms and ranking algorithms, to triangle counting and
bipartite-graph-based algorithms. The results show that on a single GPU,
Gunrock has on average at least an order of magnitude speedup over Boost and
PowerGraph, comparable performance to the fastest GPU hardwired primitives and
CPU shared-memory graph libraries such as Ligra and Galois, and better
performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing
(TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance
Graph Processing Library on the GPU
A Streaming Multi-GPU Implementation of Image Simulation Algorithms for Scanning Transmission Electron Microscopy
Simulation of atomic resolution image formation in scanning transmission
electron microscopy can require significant computation times using traditional
methods. A recently developed method, termed plane-wave reciprocal-space
interpolated scattering matrix (PRISM), demonstrates potential for significant
acceleration of such simulations with negligible loss of accuracy. Here we
present a software package called Prismatic for parallelized simulation of
image formation in scanning transmission electron microscopy (STEM) using both
the PRISM and multislice methods. By distributing the workload between multiple
CUDA-enabled GPUs and multicore processors, accelerations as high as 1000x for
PRISM and 30x for multislice are achieved relative to traditional multislice
implementations using a single 4-GPU machine. We demonstrate a potentially
important application of Prismatic, using it to compute images for atomic
electron tomography at sufficient speeds to include in the reconstruction
pipeline. Prismatic is freely available both as an open-source CUDA/C++ package
with a graphical user interface and as a Python package, PyPrismatic
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