In pushing the limits of throughput of floating-point operations, GPUs have become a unique technology. During this session, we'll explore the current state of affairs from an application perspective. For this, we'll consider different computational science areas including fundamental research on matter, materials science, and brain research. Focusing on key application performance characteristics, we review current architectural and technology trends to derive an outlook towards future GPU-accelerated architectures.
A key driver for pushing high-performance computing is the enablement of new research. One of the biggest and most exiting scientific challenge requiring high-performance computing is to decode the human brain. Many of the research topics in this field require scalable compute resources or the use of advance data analytics methods (including deep learning) for processing extreme scale data volumes. GPUs are a key enabling technology and we will thus focus on the opportunities for using these for computing, data analytics and visualisation. GPU-accelerated servers based on POWER processors are here of particular interest due to the tight integration of CPU and GPU using NVLink and the enhanced data transport capabilities.
In 2012 the NVIDIA Application Lab at Jülich has been established to work with application developers on GPU enablement. In this talk we will tour through a variety of applications and evaluate opportunities of new GPU architectures and GPU-accelerated HPC systems, in particular for data-intensive applications.
The NVIDIA Application Lab at Julich, established by JSC and NVIDIA in June 2012, aims on enabling scientific applications for GPU-based architectures. Selected applications and their performance characteristics will be presented. Strategies for multi-GPU parallelizations (necessary to meet computing demands) will be discussed.