1. Trip report: GPU Programming @ UERJ Felice Pantaleo SFT Group Meeting 03/11/2014 Felice Pantaleo SFT Group Meeting 03/11/2014

2. Eplanet visit Host institute: Universidade do Estado do Rio de Janeiro (UERJ) Duration: Two-weeks long visit from Oct 6th to 17th Topic: GPU Programming felice.pantaleo@cern.ch 2

3. First week Dedicated to installation of two nodes with two NVIDIA GTX 650 Dedicated to installation of two nodes with two NVIDIA GTX 650 CUDA 6.5 installed CUDA 6.5 installed IT specialists learning about configuration and capabilities IT specialists learning about configuration and capabilities Introductory questionnaire sent to students in order to get to know them and tailor the lectures Introductory questionnaire sent to students in order to get to know them and tailor the lectures felice.pantaleo@cern.ch 3

4. Questionnaire 4

5. Questionnaire 5

6. Questionnaire 6

7. Questionnaire 7

8. Parallel Programming intro Introduction on Parallel Programming: motivations, architectures and algorithms. Reasons Computing Systems are becoming more and more parallel and heterogeneous were explained. felice.pantaleo@cern.ch 8

9. Introduction to GPU Programming using CUDA Introduce through examples, based on the CUDA programming language, the three abstractions that make the foundations of GPU programming: - Thread hierarchy - Synchronization - Memory hierarchy/Shared Memory felice.pantaleo@cern.ch 9

10. Load Balancing and Partitioning The aim was to make the students understand the relationship between a domain problem and the computational models available. Techniques to reduce the Streaming Multiprocessors idle time by making use of dynamic scheduling and dynamic partitioning were shown. felice.pantaleo@cern.ch 10

11. Hands-on Duration: 10 hours Duration: 10 hours GPU Memory management: allocation, data transfer between host and device, synchronization Kernel launch: offload of a parallel section to the GPU Partitioning of a problem to the GPU threads Profiling of a CUDA application felice.pantaleo@cern.ch 11

12. Hands-on Making use of the GPU shared memory Making use of Asynchronous operations Reducing contention by privatization Scatter to gather Filling histograms on GPUs The interest was so high that many of the students kept working on the exercises from home! felice.pantaleo@cern.ch 12

13. Feedback The students were asked to give some feedback using an anonymous questionnaire (see backup) Very positive feedback felice.pantaleo@cern.ch 13

14. Conclusion All goals that were set before the visit were achieved Language was sometimes a problem – – Italian helped ;-) Interest in preparing a degree thesis on the topic of parallel computing for high energy physics experiment in the context of the host group Didactic material available: https://twiki.cern.ch/twiki/bin/view/Sandbox/Ep lanetPantaleoUERJ2014 felice.pantaleo@cern.ch 14

15. Didactic Material I am starting the initiative of collecting GPU Training material with some people from the HPC community (Cambridge, CINECA, BSC) I am starting the initiative of collecting GPU Training material with some people from the HPC community (Cambridge, CINECA, BSC) – Could be done in the context of the Concurrency Forum – Common github resource for trainers – Expertise acquisition from HPC community (GPUs for linear algebra, OpenACC) felice.pantaleo@cern.ch 15

16. BackupBackup felice.pantaleo@cern.ch16

17. Anonymous Feedback felice.pantaleo@cern.ch 17

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