These are just some announcements of upcoming events in which I am involved in a varying degree. The first two will be take place at RWTH Aachen University and attendance is free of charge, the second is part of the SC12 conference in Salt Lake City, UT in the US.
Tuning for bigSMP HPC Workshop – aixcelerate (October 8th – 10th, 2012). The number of cores per processor chip is increasing. Today’s “fat” compute nodes are equipped with up to 16 eight-core Intel Xeon processors, resulting in 128 phyiscal cores, with up to 2 TB of main memory. Furthermore, special solutions like a ScaleMP vSMP system may consist of 16 nodes with 4 eight-core Intel Xeon processors each and 4 TB of accumulated main memory, scaling the number of cores even further up to 1024 per machine. While message-passing with MPI is the dominating paradigm for parallel programming in the domain of high performance computing (HPC), with the growing number of cores per cluster node the combination of MPI with shared memory programming is gaining importance. The efficient use of these systems also requires NUMA-aware data management. In order to exploit different levels of parallelism, namely through shared memory programming within a node and message-passing across the nodes, obtaining good performance becomes increasingly difficult. This tuning workshop will in detail cover tools and methods to program big SMP systems. The first day will focus on OpenMP programming on big NUMA systems, the second day will focus on Intel Performance Tools as well as the ScaleMP machine, and the third day will focus on Hybrid Parallelization. Attendees are kindly requested to prepare and bring in their own code, if applicable. If you do not have an own code, but you are interested in the presented topics, you may work on prepared exercises during the lab time (hands-on). It is recommended to have good knowledge in MPI and/or OpenMP. More details and the registration link can be found at the event website.
OpenACC Tutorial Workshop (October 11th to 12th, 2012). OpenACC is a directive-based programming model for accelerators which enables delegating the responsibility for low-level (e.g. CUDA or OpenCL) programming tasks to the compiler. To this end, using the OpenACC API, the programmer can easily offload compute-intensive loops to an attached accelerator. The open industry standard OpenACC has been introduced in November 2011 and supports accelerating regions of code in standard C, C++ and Fortran. It provides portability across operating systems, host CPUs and accelerators. Up to know, OpenACC compilers exist from Cray, PGI and CAPS. During this workshop, you will work with PGI’s OpenACC implementation on Nvidia Quadro 6000 GPUs. This OpenACC workshop is divided into two parts (with separate registrations!). In the first part, we will give an introduction to the OpenACC API while focusing on GPUs. It is open for everyone who is interested in the topic. In contrast to the first part, the second part will not contain any presentations or hands-on sessions. To the second day, we invite all programmers who have their own code and want to give it a try to accelerate it on a GPU using OpenACC and with the help of our team members and Nvidia staff. More details and the registration link can be found at the event website.
Advanced OpenMP Tutorial at SC12 (November 12th, 2012). With the increasing prevalence of multicore processors, shared-memory programming models are essential. OpenMP is a popular, portable, widely supported and easy-to-use shared-memory model. Developers usually find OpenMP easy to learn. However, they are often disappointed with the performance and scalability of the resulting code. This disappointment stems not from shortcomings of OpenMP but rather with the lack of depth with which it is employed. Our “Advanced OpenMP Programming” tutorial addresses this critical need by exploring the implications of possible OpenMP parallelization strategies, both in terms of correctness and performance. While we quickly review the basics of OpenMP programming, we assume attendees understand basic parallelization concepts and will easily grasp those basics. We discuss how OpenMP features are implemented and then focus on performance aspects, such as data and thread locality on NUMA architectures, false sharing, and private versus shared data. We discuss language features in-depth, with emphasis on features recently added to OpenMP such as tasking. We close with debugging, compare various tools, and illustrate how to avoid correctness pitfalls. More details can be found on the event website.