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Dell PowerEdge C4130 Performance with

K80 GPUs - NAMD

Authors: Saeed Iqbal and Mayura Deshmukh

The field of Molecular Dynamics (MD) has seen a tremendous boost in simulation capacity since the introduction

of General Purpose GPUs. This trend is sustained by freely available feature-rich GPU-enabled molecular

dynamics simulators like NAMD. For more information on NAMD, please visit

http://www.ks.uiuc.edu/Research/namd/ and for more information on GPUs visit http://www.nvidia.com/tesla.

Things only get better for NAMD with the release of the new Tesla K80 GPUs from NVIDIA. K80 offers significant

improvements over the previous model the K40. From the HPC prospective the most important improvement is

the 1.87 TFLOPs (double precision) compute capacity, which is about 30% more than K40. The auto-boost feature

in K80 automatically provides additional performance if additional power head room is available. The internal

GPUs are based on the GK210 architecture and have a total of 4,992 cores which represent a 73% improvement

over K40. The K80 has a total memory of 24GBs which is divided equally between the two internal GPUs; this is a

100% more memory capacity compared to the K40. The memory bandwidth in K80 is improved to 480 GB/s. The

rated power consumption of a single K80 is a maximum of 300 watts.

Combining K80s with the latest high GPU density design from Dell, the PowerEdge C4130, results in an extra-

ordinarily powerful compute node. The C4130 can be configured with up to four K40 or K80 GPUs in a 1U form

factor. Also the uniqueness of PowerEdge C4130 is that it offers several workload specific configurations,

potentially making it a better fit, for MD codes in general , and specifically for NAMD.

The PowerEdge C4130 offers five configurations, noted here as “A” through “E”. Part of the goal of this blog is to

find out which configuration is best suited for NAMD. The three quad GPU configurations “A”, “B” and “C” are

compared. Also the two dual GPU configurations “D” and “E” are compared for users interested in lower GPU

density of 2 GPU per 1 rack unit. The first two quad GPU configurations (“A” & “B”) have an internal PCIe switch

module which allows seamless peer to peer GPU communication. We also want to understand the impact of the

switch module on NAMD. Figure 1 below shows the block diagrams for configurations A to E

Summary of content (4 pages)

PAGE 1
Dell PowerEdge C4130 Performance with K80 GPUs - NAMD Authors: Saeed Iqbal and Mayura Deshmukh The field of Molecular Dynamics (MD) has seen a tremendous boost in simulation capacity since the introduction of General Purpose GPUs. This trend is sustained by freely available feature-rich GPU-enabled molecular dynamics simulators like NAMD. For more information on NAMD, please visit http://www.ks.uiuc.edu/Research/namd/ and for more information on GPUs visit http://www.nvidia.com/tesla.
PAGE 2
Figure 1: C4130 Configuration Block Diagram In this blog we evaluate improved NAMD performance with Tesla K80 GPUs. Two proteins F1ATPASE and STMV, which consist of 327K and 1066K atoms respectively, have been selected due to their relatively large problem size. The performance measure is in “days/ns”, which shows the number of days required to simulate 1 nanosecond of real-time. Table 1 gives more information about the hardware configuration and application details used for the tests.
PAGE 3
Figure 2: NAMD performance and acceleration on the five C4130 configurations. Figure 2 shows the performance of NAMD on the PowerEdge C4130. Configurations “A”, “B” and “C” are four GPU configurations. However the acceleration on NAMD also seems to be sensitive to number of CPUs, e.g., there is a significant difference in the acceleration between “A” and “B”. “B” has an additional CPU compared to “A”. Among the three quad GPU configurations the current version of NAMD performs best on configuration “C”.
PAGE 4
Figure 3 shows the power consumption results of running NAMD for the runs in Figure 2. As shown, the power consumption for quad GPU configurations is about 2.1X to 2.3X resulting in accelerations from 4.4X to 7.8X. Configuration “C” does the best from performance per watt perspective (an acceleration of 7.8X for 2.3X more power). The power consumption of “D” is higher than “E” due to higher due to the additional CPU and improved utilization of GPUs resulting in better acceleration.