Held in conjunction with SC17: The International Conference on High Performance Computing, Networking, Storage and Analysis, in cooperation with TCHPC: The IEEE Computer Society Technical Consortium on High Performance Computing Over the last decades an incredible amount of resources has been devoted to building ever more powerful supercomputers. However, exploiting the full capabilities of these machines is becoming exponentially more difficult with each new generation of hardware. To help understand and optimize the behavior of massively parallel simulations the performance analysis community has created a wide range of tools and APIs to collect performance data, such as flop counts, network traffic or cache behavior at the largest scale. However, this success has created a new challenge, as the resulting data is far too large and too complex to be analyzed in a straightforward manner.

Therefore, new automatic analysis and visualization approaches must be developed to allow application developers to intuitively understand the multiple, interdependent effects that their algorithmic choices have on the final performance. This workshop will bring together researchers from the fields of performance analysis and visualization to discuss new approaches of applying visualization and visual analytics techniques to large scale applications.