Presentation

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presentation/jrwtc2019/texs/abstract.tex

+\begin{abstract}
+  Recent computing platforms combine CPUs with different types of
+  accelerators such as Graphical Processing Units ({\it GPUs}) to cope
+  with the increasing computation power needed by complex real-time
+  applications. NVIDIA
+  GPUs are compound of hundreds of computing elements called {\it CUDA
+    cores}, to achieve fast computations for parallel applications. 
+    
+  However, GPUs are not designed to support real-time
+  execution, as their main goal is to achieve maximum throughput for their resources.  Supporting real-time
+  execution on NVIDIA GPUs involves not only achieving timely
+  predictable calculations but also to optimize the CUDA cores usage.
+
+  In this work, we present the design and the implementation of {\it
+    PRUDA} (Predictable Real-time CUDA), a programming platform to
+  manage the GPU resources, therefore decide when and where a
+  real-time task is executed. PRUDA is written in {\sf C} and provides
+  different mechanisms to manage the task priorities and allocation on
+  the GPU. It provides tools to help a designer to properly implement
+  real-time schedulers on the top of CUDA.
+\end{abstract}
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