Julian Merten. GPU Computing and Alternative Architecture
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1 Future Directions of Cosmological Simulations / Edinburgh 1 / 16 Julian Merten GPU Computing and Alternative Architecture Institut für Theoretische Astrophysik Zentrum für Astronomie Universität Heidelberg November 9 th, 2010
2 Modern HPC: Parallelism Future Directions of Cosmological Simulations / Edinburgh 2 / 16
3 Modern HPC: Parallelism Future Directions of Cosmological Simulations / Edinburgh 2 / 16
4 HPC extreme: Jaguar at Oak Ridge NL Jaguar nodes AMD GHz cores 300 TB main memory optical Infiniband Future Directions of Cosmological Simulations / Edinburgh 3 / 16
5 Future Directions of Cosmological Simulations / Edinburgh 4 / 16 But: The EχaScale! Current systems reach O(10MW) Standard CPU design it at the edge of doable Flops/Watt No problem: Add more nodes! Hehe...no : O(Eflop) O(10GW) Better: Use the same amount of nodes but make them more powerful Hybrid model with accelerated nodes. Unfortunately this comes not for free: Additional layer of optimised implementation on each node.
6 Future Directions of Cosmological Simulations / Edinburgh 4 / 16 But: The EχaScale! Current systems reach O(10MW) Standard CPU design it at the edge of doable Flops/Watt No problem: Add more nodes! Hehe...no : O(Eflop) O(10GW) Better: Use the same amount of nodes but make them more powerful Hybrid model with accelerated nodes. Unfortunately this comes not for free: Additional layer of optimised implementation on each node.
7 Future Directions of Cosmological Simulations / Edinburgh 4 / 16 But: The EχaScale! Current systems reach O(10MW) Standard CPU design it at the edge of doable Flops/Watt No problem: Add more nodes! Hehe...no : O(Eflop) O(10GW) Better: Use the same amount of nodes but make them more powerful Hybrid model with accelerated nodes. Unfortunately this comes not for free: Additional layer of optimised implementation on each node.
8 Future Directions of Cosmological Simulations / Edinburgh 4 / 16 But: The EχaScale! Current systems reach O(10MW) Standard CPU design it at the edge of doable Flops/Watt No problem: Add more nodes! Hehe...no : O(Eflop) O(10GW) Better: Use the same amount of nodes but make them more powerful Hybrid model with accelerated nodes. Unfortunately this comes not for free: Additional layer of optimised implementation on each node.
9 Future Directions of Cosmological Simulations / Edinburgh 4 / 16 But: The EχaScale! Current systems reach O(10MW) Standard CPU design it at the edge of doable Flops/Watt No problem: Add more nodes! Hehe...no : O(Eflop) O(10GW) Better: Use the same amount of nodes but make them more powerful Hybrid model with accelerated nodes. Unfortunately this comes not for free: Additional layer of optimised implementation on each node.
10 Future Directions of Cosmological Simulations / Edinburgh 4 / 16 But: The EχaScale! Current systems reach O(10MW) Standard CPU design it at the edge of doable Flops/Watt No problem: Add more nodes! Hehe...no : O(Eflop) O(10GW) Better: Use the same amount of nodes but make them more powerful Hybrid model with accelerated nodes. Unfortunately this comes not for free: Additional layer of optimised implementation on each node.
11 Future Directions of Cosmological Simulations / Edinburgh 4 / 16 But: The EχaScale! Current systems reach O(10MW) Standard CPU design it at the edge of doable Flops/Watt No problem: Add more nodes! Hehe...no : O(Eflop) O(10GW) Better: Use the same amount of nodes but make them more powerful Hybrid model with accelerated nodes. Unfortunately this comes not for free: Additional layer of optimised implementation on each node.
12 Future Directions of Cosmological Simulations / Edinburgh 5 / 16 The advent of GPU s...or the art of shooting monsters Carmack et al. 1993
13 Future Directions of Cosmological Simulations / Edinburgh 5 / 16 The advent of GPU s...or the art of shooting monsters Carmack et al Carmack et al. 2004
14 Future Directions of Cosmological Simulations / Edinburgh 6 / 16 A massively parallel chip design You have to keep those cores busy. Extreme parallelisation by SIMT concept, similar to the vector concept. Split a problem into thousands/millions of identical threads.
15 Threads must be simple and well-ordered From NVIDIA CUDA C Programming Guide Rules Blocks run on MP s Keep cores on MP busy with threads Sync only possible within block Use memory hierarchy (register, shared/l1, global) Threads must be close to the data Do not disturb the collective. Find balance between parallelisation and work per thread Future Directions of Cosmological Simulations / Edinburgh 7 / 16
16 Finally an example Future Directions of Cosmological Simulations / Edinburgh 8 / 16
17 Finally an example Future Directions of Cosmological Simulations / Edinburgh 8 / 16
18 Finally an example Future Directions of Cosmological Simulations / Edinburgh 8 / 16
19 Finally an example Future Directions of Cosmological Simulations / Edinburgh 8 / 16
20 Finally an example Future Directions of Cosmological Simulations / Edinburgh 8 / 16
21 Finally an example Future Directions of Cosmological Simulations / Edinburgh 8 / 16
22 Finally an example Future Directions of Cosmological Simulations / Edinburgh 8 / 16
23 Finally an example Future Directions of Cosmological Simulations / Edinburgh 8 / 16
24 Finally an example Future Directions of Cosmological Simulations / Edinburgh 8 / 16
25 Finally an example Future Directions of Cosmological Simulations / Edinburgh 8 / 16
26 Future Directions of Cosmological Simulations / Edinburgh 9 / 16 NVIDIA Tesla C Mflops peak in single NO double 1.5 GB global memory 16 MPs 128 cores 8 KB register size 16KB shared memory
27 Future Directions of Cosmological Simulations / Edinburgh 9 / 16 NVIDIA Tesla C Gflop peak in single A lot less in double 4 GB global memory 30 MPs 240 cores 16 KB register size 16KB shared memory
28 Future Directions of Cosmological Simulations / Edinburgh 9 / 16 NVIDIA Tesla C/M2050 (Fermi) 1 Gflop peak in single 500 Mflops peak in double 3 GB ECC global memory 14 (16) MPs 448 (512) cores 32 KB register size 48 KB shared memory
29 Future Directions of Cosmological Simulations / Edinburgh 10 / 16 Tuning tips: The GPU is a funny beast Make sure that you have enough threads per block to keep the MP cores busy. Always keep the problem in GPU memory, never-ever even think about node memory access. Use memory coalescence and shared memory. Avoid global GPU memory within block calculations. Know your thread scheduler, it enables you to use the word warp in a scientific talk. Avoid thread divergence, no conditionals in the threads. Exploit the parameter space (which is large in GPU programming). Try to stay calm when NVIDIA changes the architecture and you have to start from scratch.
30 Future Directions of Cosmological Simulations / Edinburgh 11 / 16 Programming models: CUDA and OpenCL CUDA C available from NVIDIA and CUDA Fortran available from PGI (commercial). Pros Full control over all features. Achieves best performance. Large set of libraries already available (CUBLAS, CUFFT, CUSPARSE, CURAND, CULA). Cons Learning curve. Limited to NVIDIA architecture. Separate compiler. My thoughts on OpenCL: Basically identical to CUDA, open, but harder to learn (compare CUDA Driver API).
31 Future Directions of Cosmological Simulations / Edinburgh 12 / 16 Programming models: Directives Available from PGI and HMPP (commercial). Pros Much easier to learn, similar to OpenMP code. Codes are much easy to port. Might be the model for the future. Cons Slower than CUDA and harder to tune. By now, much worse documented and accepted by the community. Not ready for large production runs.
32 Future Directions of Cosmological Simulations / Edinburgh 13 / 16 Alternative architectures: IBM Cell Not really my field, sorry. Powers the PlayStation3. Eight core design. Peaked with Roadrunner. Development and programming models available. Not wide-spread.
33 Future Directions of Cosmological Simulations / Edinburgh 14 / 16 Alternative architectures: Intel Knight s Ferry Courtesy of Intel Corp. 32 x GHz 4 threads per core PCI-E interface to host C(++), Fortran compilers Better behaved regarding cache coherence and core synchronisation Might become the main competitor to the Fermi architecture Designs with >50 cores are planned, Knight s Corner
34 Future Directions of Cosmological Simulations / Edinburgh 14 / 16 Alternative architectures: Intel Knight s Ferry Courtesy of Intel Corp. 32 x GHz 4 threads per core PCI-E interface to host C(++), Fortran compilers Better behaved regarding cache coherence and core synchronisation Might become the main competitor to the Fermi architecture Designs with >50 cores are planned, Knight s Corner
35 Future Directions of Cosmological Simulations / Edinburgh 15 / 16 Alternative architectures: FPGA s MPRACE board using a Xilinx FPGA chip, as e.g. used in ARI Heidelberg Calculations are basically hard-wired on-chip Extremely high performance on extremely specialised hardware LHC uses those devices for triggering and event-selection Disadvantages obvious: You need to be a specialist, to set-up, run and maintain those systems Only few systems are available
36 Conclusions 1 Extreme parallelisation on the node achieves tremendous speed-ups on special hardware. 2 Fermi GPUs seem to be the preferred accelerator model at the moment. Curious for Knight s Ferry (Corner). 3 CUDA is probably the best way to programme them. 4 Already small/medium size systems are very powerful (e.g node systems in Heidelberg). Accelerated single-node systems might be even more useful (analyses etc.). 5 Nevertheless, porting and designing codes is not straight-forward and involves a lot of specific knowledge. A GPU version of e.g. Gadget will look very different and yes, you can trash your career with this. 6 Many core programming is not completely mature yet, software but esp. hardware is still developing. Anyway, esp. cosmology is about to miss the HPC train. 7 Whoever is interested in a collaboration on a GPU project: Let me know. Future Directions of Cosmological Simulations / Edinburgh 16 / 16
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