Increasing Cluster Performance by Combining rCUDA with Slurm
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Federico Silla from the Technical University of Valencia presented this deck at the Switzerland HPC Conference. "Graphics Processing Units (GPUs) are currently used in data centers to reduce the execution time of compute-intensive applications. However, the use of GPUs presents several side ef- fects, such as increased acquisition costs as well as larger space requirements. Furthermore, GPUs require a non-negligible amount of energy even while idle. Additionally, GPU utilization is usually low for most applications. The use of virtual GPUs may address these concerns. In this regard, the remote GPU virtualization mechanism could be leveraged to share the GPUs present in the computing facility among the nodes of the cluster. This would increase overall GPU utilization, thus reducing the negative impact of the increased costs mentioned before. Reducing the amount of GPUs installed in the cluster could also be possible. In this talk we present the remote GPU virtualization mechanism using as case study the performance attained by a cluster using the rCUDA middleware and a modified version of the Slurm sched- uler, which is able to map remote virtual GPUs to jobs. By leveraging rCUDA+Slurm, cluster throughput, measured as jobs completed per time unit, is doubled at the same time that total energy consumption is reduced up to 40%. GPU utilization is also increased." Watch the video presentation: https://www.youtube.com/watch?v=yQWiQiyFpAg See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/ Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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Increasing Cluster Performance by Combining rCUDA with Slurm
- 1. Increasing cluster performance by combining rCUDA with Slurm Federico Silla Technical University of Valencia Spain
- 2. HPC Advisory Council Switzerland Conference 2016 2/56 Outline rCUDA … what’s that?
- 3. HPC Advisory Council Switzerland Conference 2016 3/56 Basics of CUDA GPU
- 4. HPC Advisory Council Switzerland Conference 2016 4/56 rCUDA … remote CUDA No GPU
- 5. HPC Advisory Council Switzerland Conference 2016 5/56 A software technology that enables a more flexible use of GPUs in computing facilities No GPU rCUDA … remote CUDA
- 6. HPC Advisory Council Switzerland Conference 2016 6/56 Basics of rCUDA
- 7. HPC Advisory Council Switzerland Conference 2016 7/56 Basics of rCUDA
- 8. HPC Advisory Council Switzerland Conference 2016 8/56 Basics of rCUDA
- 9. HPC Advisory Council Switzerland Conference 2016 9/56 Physical configuration CPU MainMemory Network PCI-e CPU MainMemory Network PCI-e CPU MainMemory Network PCI-e CPU MainMemory Network PCI-e CPU MainMemory Network PCI-e PCI-e CPU MainMemory Network Interconnection Network Logical connections Logical configuration Cluster envision with rCUDA GPU GPU mem GPU GPU mem GPU GPU mem GPU GPU mem GPU GPU mem GPU GPU mem GPU GPU mem GPU GPU mem GPU GPU mem GPU GPU mem GPU GPU mem GPU GPU mem GPU GPU mem GPU GPU mem PCI-e CPU MainMemory Network Interconnection Network PCI-e CPU GPU GPU mem MainMemory Network GPU GPU mem PCI-e CPU GPU GPU mem MainMemory Network GPU GPU mem CPU MainMemory Network GPU GPU mem GPU GPU mem PCI-e CPU MainMemory Network GPU GPU mem GPU GPU mem PCI-e CPU MainMemory Network GPU GPU mem GPU GPU mem PCI-e rCUDA allows a new vision of a GPU deployment, moving from the usual cluster configuration: to the following one:
- 10. HPC Advisory Council Switzerland Conference 2016 10/56 Outline Two questions: • Why should we need rCUDA? • rCUDA … slower CUDA?
- 11. HPC Advisory Council Switzerland Conference 2016 11/56 Outline Two questions: • Why should we need rCUDA? • rCUDA … slower CUDA?
- 12. HPC Advisory Council Switzerland Conference 2016 12/56 The main concern with rCUDA is the reduced bandwidth to the remote GPU Concern with rCUDA No GPU
- 13. HPC Advisory Council Switzerland Conference 2016 13/56 Using InfiniBand networks
- 14. HPC Advisory Council Switzerland Conference 2016 14/56 H2D pageable D2H pageable H2D pinned D2H pinned rCUDA EDR Orig rCUDA EDR Opt rCUDA FDR Orig rCUDA FDR Opt Initial transfers within rCUDA
- 15. HPC Advisory Council Switzerland Conference 2016 15/56 CUDASW++ Bioinformatics software for Smith-Waterman protein database searches 144 189 222 375 464 567 657 729 850 1000 1500 2005 2504 3005 3564 4061 4548 4743 5147 5478 0 5 10 15 20 25 30 35 40 45 0 2 4 6 8 10 12 14 16 18 FDR Overhead QDR Overhead GbE Overhead CUDA rCUDA FDR rCUDA QDR rCUDA GbE Sequence Length rCUDAOverhead(%) ExecutionTime(s) Performance depending on network
- 16. HPC Advisory Council Switzerland Conference 2016 16/56 H2D pageable D2H pageable H2D pinned Almost 100% of available BW D2H pinned Almost 100% of available BW rCUDA EDR Orig rCUDA EDR Opt rCUDA FDR Orig rCUDA FDR Opt Optimized transfers within rCUDA
- 17. HPC Advisory Council Switzerland Conference 2016 17/56 rCUDA optimizations on applications • Several applications executed with CUDA and rCUDA • K20 GPU and FDR InfiniBand • K40 GPU and EDR InfiniBand Lower is better
- 18. HPC Advisory Council Switzerland Conference 2016 18/56 Outline Two questions: • Why should we need rCUDA? • rCUDA … slower CUDA?
- 19. HPC Advisory Council Switzerland Conference 2016 19/56 Outline rCUDA improves cluster performance
- 20. HPC Advisory Council Switzerland Conference 2016 20/56 Dual socket E5-2620v2 Intel Xeon + 32GB RAM + K20 GPU FDR InfiniBand based cluster Test bench for studying rCUDA+Slurm node with the Slurm scheduler node with the Slurm scheduler node with the Slurm scheduler 8+1 GPU nodes 16+1 GPU nodes 4+1 GPU nodes
- 21. HPC Advisory Council Switzerland Conference 2016 21/56 Applications used for tests: GPU-Blast (21 seconds; 1 GPU; 1599 MB) LAMMPS (15 seconds; 4 GPUs; 876 MB) MCUDA-MEME (165 seconds; 4 GPUs; 151 MB) GROMACS (2 nodes) (167 seconds) NAMD (4 nodes) (11 minutes) BarraCUDA (10 minutes; 1 GPU; 3319 MB) GPU-LIBSVM (5 minutes; 1GPU; 145 MB) MUMmerGPU (5 minutes; 1GPU; 2804 MB) Non-GPU Short execution time Long execution time Set 1 Set 2 Three workloads: Set 1 Set 2 Set 1 + Set 2 Applications for studying rCUDA+Slurm
- 22. HPC Advisory Council Switzerland Conference 2016 22/56 Workloads for studying rCUDA+Slurm (I)
- 23. HPC Advisory Council Switzerland Conference 2016 23/56 Performance of rCUDA+Slurm (I)
- 24. HPC Advisory Council Switzerland Conference 2016 24/56 Workloads for studying rCUDA+Slurm (II)
- 25. HPC Advisory Council Switzerland Conference 2016 25/56 Performance of rCUDA+Slurm (II)
- 26. HPC Advisory Council Switzerland Conference 2016 26/56 Outline Why does rCUDA improve cluster performance?
- 27. HPC Advisory Council Switzerland Conference 2016 27/56 Interconnection Network node nnode 2 node 3node 1 Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM • Non-accelerated applications keep GPUs idle in the nodes where they use all the cores 1st reason for improved performance A CPU-only application spreading over these nodes will make their GPUs unavailable for accelerated applications Hybrid MPI shared-memory non-accelerated applications usually span to all the cores in a node (across n nodes)
- 28. HPC Advisory Council Switzerland Conference 2016 28/56 Interconnection Network node nnode 2 node 3node 1 Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM • Accelerated applications keep CPUs idle in the nodes where they execute An accelerated application using just one CPU core may avoid other jobs to be dispatched to this node Hybrid MPI shared-memory non-accelerated applications usually span to all the cores in a node (across n nodes) 2nd reason for improved performance (I)
- 29. HPC Advisory Council Switzerland Conference 2016 29/56 Hybrid MPI shared-memory non-accelerated applications usually span to all the cores in a node (across n nodes) Interconnection Network node nnode 2 node 3node 1 Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM • Accelerated applications keep CPUs idle in the nodes where they execute An accelerated MPI application using just one CPU core per node may keep part of the cluster busy 2nd reason for improved performance (II)
- 30. HPC Advisory Council Switzerland Conference 2016 30/56 • Do applications completely squeeze the GPUs available in the cluster? • When a GPU is assigned to an application, computational resources inside the GPU may not be fully used • Application presenting low level of parallelism • CPU code being executed (GPU assigned ≠ GPU working) • GPU-core stall due to lack of data • etc … Interconnection Network node nnode 2 node 3node 1 Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM Network GPU PCIe CPU CPU RAM RAM RAM 3rd reason for improved performance
- 31. HPC Advisory Council Switzerland Conference 2016 31/56 GPU usage of GPU-Blast GPU assigned but not used GPU assigned but not used
- 32. HPC Advisory Council Switzerland Conference 2016 32/56 GPU usage of CUDA-MEME GPU utilization is far away from maximum
- 33. HPC Advisory Council Switzerland Conference 2016 33/56 GPU usage of LAMMPS GPU assigned but not used
- 34. HPC Advisory Council Switzerland Conference 2016 34/56 GPU allocation vs GPU utilization GPUs assigned but not used
- 35. HPC Advisory Council Switzerland Conference 2016 35/56 Sharing a GPU among jobs: GPU-Blast Two concurrent instances of GPU-Blast One instance required about 51 seconds
- 36. HPC Advisory Council Switzerland Conference 2016 36/56 Two concurrent instances of GPU-Blast Sharing a GPU among jobs: GPU-Blast First instance
- 37. HPC Advisory Council Switzerland Conference 2016 37/56 Two concurrent instances of GPU-Blast Sharing a GPU among jobs: GPU-Blast First instance Second instance
- 38. HPC Advisory Council Switzerland Conference 2016 38/56 Sharing a GPU among jobs • LAMMPS: 876 MB • mCUDA-MEME: 151 MB • BarraCUDA: 3319 MB • MUMmerGPU: 2104 MB • GPU-LIBSVM: 145 MB K20 GPU
- 39. HPC Advisory Council Switzerland Conference 2016 39/56 Outline Other reasons for using rCUDA?
- 40. HPC Advisory Council Switzerland Conference 2016 40/56 Cheaper cluster upgrade No GPU • Let’s suppose that a cluster without GPUs needs to be upgraded to use GPUs • GPUs require large power supplies • Are power supplies already installed in the nodes large enough? • GPUs require large amounts of space • Does current form factor of the nodes allow to install GPUs? The answer to both questions is usually “NO”
- 41. HPC Advisory Council Switzerland Conference 2016 41/56 Cheaper cluster upgrade No GPU GPU-enabled Approach 1: augment the cluster with some CUDA GPU- enabled nodes only those GPU-enabled nodes can execute accelerated applications
- 42. HPC Advisory Council Switzerland Conference 2016 42/56 Cheaper cluster upgrade Approach 2: augment the cluster with some rCUDA servers all nodes can execute accelerated applications GPU-enabled
- 43. HPC Advisory Council Switzerland Conference 2016 43/56 Dual socket E5-2620v2 Intel Xeon + 32GB RAM + K20 GPU FDR InfiniBand based cluster 16 nodes without GPU + 1 node with 4 GPUs Cheaper cluster upgrade
- 44. HPC Advisory Council Switzerland Conference 2016 44/56 More workloads for studying rCUDA+Slurm
- 45. HPC Advisory Council Switzerland Conference 2016 45/56 Performance -68% -60% -63% -56% +131% +119%
- 46. HPC Advisory Council Switzerland Conference 2016 46/56 Outline Additional reasons for using rCUDA?
- 47. HPC Advisory Council Switzerland Conference 2016 47/56 #1: More GPUs for a single application 64 GPUs!
- 48. HPC Advisory Council Switzerland Conference 2016 48/56 MonteCarlo Multi-GPU (from NVIDIA samples) Lower is better Higher is better #1: More GPUs for a single application FDR InfiniBand + NVIDIA Tesla K20
- 49. HPC Advisory Council Switzerland Conference 2016 49/56 #2: Virtual machines can share GPUs • The GPU is assigned by using PCI passthrough exclusively to a single virtual machine • Concurrent usage of the GPU is not possible
- 50. HPC Advisory Council Switzerland Conference 2016 50/56 High performance network available Low performance network available #2: Virtual machines can share GPUs
- 51. HPC Advisory Council Switzerland Conference 2016 51/56 Box A has 4 GPUs but only one is busy Box B has 8 GPUs but only two are busy 1. Move jobs from Box B to Box A and switch off Box B 2. Migration should be transparent to applications (decided by the global scheduler) #3: GPU task migration Box A Box B Migration is performed at GPU granularity
- 52. HPC Advisory Council Switzerland Conference 2016 52/56 1 1 3 7 13 14 14 Job granularity instead of GPU granularity #3: GPU task migration
- 53. HPC Advisory Council Switzerland Conference 2016 53/56 Outline … in summary …
- 54. HPC Advisory Council Switzerland Conference 2016 54/56 • Cons: 1.Reduced bandwidth to remote GPU (really a concern??) Pros and cons of rCUDA • Pros: 1.Many GPUs for a single application 2.Concurrent GPU access to virtual machines 3.Increased cluster throughput 4.Similar performance with smaller investment 5.Easier (cheaper) cluster upgrade 6.Migration of GPU jobs 7.Reduced energy consumption 8.Increased GPU utilization
- 55. HPC Advisory Council Switzerland Conference 2016 55/56 Get a free copy of rCUDA at http://www.rcuda.net @rcuda_ More than 650 requests world wide rCUDA is a development by Technical University of Valencia
- 56. HPC Advisory Council Switzerland Conference 2016 56/56 Thanks! Questions? rCUDA is a development by Technical University of Valencia