Registration Room: E3

Welcome to PDC and the Summer School

• Erwin Laure (PDC-HPC)

Room: E3

Concepts and Algorithms for Scientific Computing

• Björn ENGQUIST

Room: E3

Introduction to PDC's Environment

• Izhar UL HASSAN

Room: E3

Lab: Introduction to PDC's Environment

• Izhar UL HASSAN

Room: Room: 4V2Röd, 4V3Ora

High-Performance Computer Architecture

• Erik HAGERSTEN

Room: E3

High-Performance Computer Architecture

• Erik HAGERSTEN

High-Performance Computer Architecture

• Erik HAGERSTEN

Room: E3

Shared memory programming, OpenMP

• Thomas ERICSSON

Two common ways of speeding up programs are to use processes or threads executing in parallel. The second week of the Summer School presents process-based parallel computing using MPI. In this lecture parallelization using threads will be discussed. Threads can be managed in different ways, e.g. using a low level library, like the POSIX threads library. The lecture deals with OpenMP, which is a more convenient way to use threads. OpenMP is a specification for a set of compiler directives, library routines, and environment variables that can be used to specify shared memory parallelism in Fortran and C/C++ programs. The threaded program can be executed on a shared memory computer (e.g. a multi- core processor). Typically, the iterations of a time consuming loop are shared among a team of threads, each thread working on part of the iterations. The loop is parallelized by writing a directive in the code and a compiler or a pre- processor will generate parallel code. Using short code examples, the lecture covers the most important OpenMP-constructs, how to use them and how not to use them. A few more realistic examples are presented as well.

Shared memory programming, OpenMP

• Thomas ERICSSON

Room: E3

Shared memory programming, OpenMP

• Thomas ERICSSON

Room: E3 T

Lab: Programming Exercises on OpenMP

• Niclas JANSSON
• Stefano MARKIDIS

Room: 4V2Röd, 4V3Ora

Lab: Programming Exercises on OpenMP

• Stefano MARKIDIS
• Niclas JANSSON

Room: 4V2Röd, 4V3Ora

Code optimization

• Thomas ERICSSON

Room: E3 In this lecture techniques for speeding up code on a uniprocessor are presented. The lecture covers basic optimization principles such as using the memory hierarchy in an efficient way. Using short code examples, the lecture covers stride minimization, blocking, loop- fusion, loop- splitting and other approaches. The choice of programming language (Fortran or C), and compiler will be discussed. There are some hints of how to tune Matlab programs. Other topics are vector arithmetic for floating point and elementary functions, virtual memory, files and the LAPACK and BLAS libraries.

Code optimization

• Thomas ERICSSON

Room: E3

Lab: OpenMP Advanced Project

• Niclas JANSSON
• Stefano MARKIDIS

Room: 4V2Röd, 4V3Ora

Lab: OpenMP Advanced Project

• Niclas JANSSON
• Stefano MARKIDIS

Room: 4V2Röd, 4V3Ora

Wrap up: OpenMP Advanced Project

• Stefano MARKIDIS
• Niclas JANSSON

Room: E3

GPU Architectures for Non-Graphics People

• David BLACK-SCHAFFER

Room: E3 Today everyone is positioning GPUs for general purpose computing. They claim that you can get 10-100x speedups over conventional CPUs, and sometimes they're even right. However, to get the most out of current- (and next-) generation GPUs, one needs to understand the architectural differences and how they effect your choice of algorithm. In this talk I will cover GPU architecture in comparison to current CPUs, discuss the implications for getting good performance, and introduce OpenCL as a general-purpose programming language for accessing GPUs and CPUs today.

GPUs: The Hype, The Reality, and The Future

• David Black-Schaffer

Room: E3 We'll take a look at the promise and the results of porting applications to GPUs and try to distinguish the hype from the reality. We'll take a closer look at where GPUs fit into HPC today and in the near future, and try to do a fair comparison of GPU optimized programs, CPU optimized programs, and even take a look at competing accelerators such as the Intel Xeon Phi. The goal of this lecture is to give you some perspective on GPU technology and where it may (or may not) fit into HPC in the next few years.

Introduction to CUDA

• Michael SCHLIEPHAKE

Room: E3 CUDA is a parallel computing platform and programming model for NVIDIA GPU devices that allows to use the capabilities of the hardware efficiently. Lecture and lab give an overview of CUDA, discuss aspects of programming that needs the programmer's attention in order to gain the desired performance, and provide a first hands-on experience.

Lab: CUDA

• Michael SCHLIEPHAKE

Room: 4V2Röd, 4V3Ora

Introduction to CUDA

• Michael SCHLIEPHAKE

Room: E3

Introduction to CUDA

• Michael SCHLIEPHAKE

Room: E3

Lab: CUDA

• Michael SCHLIEPHAKE

Room: 4V2Röd, 4V3Ora

MPI Introduction

• Erwin LAURE

Room: E3

MPI Basic Concepts & Point-to-Point Communication

• Erwin LAURE

Room: E3

Lab: MPI Part 1

• Jonathan VINCENT

Room: 4V2Röd, 4V3Ora

Lab: MPI Part 1

• Jonathan VINCENT

Room: 4V2Röd, 4V3Ora

MPI - Collective Communication

• Erwin LAURE

Room: E3

MPI - Intermediate MPI

• Erwin LAURE

Room: E3

Performance Engineering

• Pekka MANNINEN

Room: E3

Performance Engineering

• Pekka MANNINEN

Room: E3

Lab: Performance Engineering

• Pekka MANNINEN

Room: 4V2Röd, 4V3Ora

Wrap up: Performance Engineering

• Pekka MANNINEN

Room: E3

MPI: Advanced Concepts

• Erwin LAURE

Room: E3

MPI: Advanced Concepts

• Erwin LAURE

Parallel Performance Engineering

• Pekka MANNINEN

Room: E3

Lab: Parallel Performance Engineering

• Pekka MANNINEN

Room: 4V2Röd, 4V3Ora

Lab: MPI Part 2

• Jonathan VINCENT

Interconnection Networks

• Michael SCHLIEPHAKE

Room: E3 Interconnect networks have large influence on the performance and efficiency of message-passing and File-I/O operations in cluster systems. The lecture presents concise information about interconnect networks in order to support the understanding of their influence and how to use it in favour of simulation applications.

Interconnection Networks

• Michael SCHLIEPHAKE

Room: E3

Case Study: Numerical experiments of turbulent flows on supercomputers

• Philipp SCHLATTER (KTH)

Room: E3 After a short description about the activities of the Swedish e-Science Research Centre (SeRC) a general motivation of studying turbulence is given, including elucidating the large range of scales needed to be resolved for high Reynolds numbers. The infrastructure needed to perform these simulations are discussed, including the simulation codes and large scale computational resources. Examples of such large scale simulations are then given in terms of high Reynolds number turbulent boundary layers on a flat plate, and also such a boundary layer with an obstacle. Finally the possibility to scale up the simulations to flows of direct aeronautical interest is discussed, e.g. when will it be possible to replace typical university wind tunnel experiments with DNS? This begs the question of the possibility of exa-scale computing and questions if the current methods can be used on such e-Infrastructure is posed?

Lab: MPI Part 3

• Jonathan VINCENT

Room: 4V2Röd, 4V3Ora

Future Programming Languages

• Stefano MARKIDIS

Room: E3 The lecture presents the current limitations of traditional programming models, such as MPI and openMP, and provides motivations to adopt new programming models for parallel computing. The potential of innovative programming models such as the Partitioned Global Address Space (PGAS) languages, and task-based programming models is discussed. Few examples of how to test on Lindgren these innovative programming models are also presented.

Future Programming Languages

• Stefano MARKIDIS

Room: E3

Abstractions for processing large data sets

• Jonas YNGVESSON

Room: E3 At Google there is often a need to process very large data sets across many machines. Building efficient parallel processing programs is not trivial and to avoid the overhead of each engineer reinventing the wheel, Google has created several programming models to abstract away the complexities of parallelism and have the programmer concentrate on the core of his processing problem instead. I will talk mainly about the MapReduce model, how it works and the conceptual model the programmer has to work with when using it. I will also mention a bit about a more recent framework, Pregel, which is built for processing very large

Open Lab and Project Work Room: 4V2Röd, 4V3Ora