Author: Yogendra Sharma, Ashish Singh, September 2016 (HPC Innovation Lab)

This blog describes the performance analysis on a PowerEdge R930 server powered by four Intel Xeon E7-8890 v4 @2.2GHz processors (code named as Broadwell-EX). Primary objective of this blog is to compare the performance of HPL, STREAM and few scientific applications ANSYS Fluent and WRF with the previous generation of Intel processor Intel Xeon E7-8890 v3 @2.5GHz codenamed Haswell-EX. Below are the configurations used for this study.

Platform

PowerEdge R930

PowerEdge R930

Processor

4 x Intel Xeon E7-8890 v3@2.5GHz (18 cores) 45MB L3 cache 165W

4 x Intel Xeon E7-8890 v4@2.2GHz (24 cores) 60MB L3 cache 165W

Memory

1024 GB = 64 x 16GB DDR4 @2400MHz RDIMMS

1024 GB = 32 x 32GB DDR4 @2400MHz RDIMMS

BIOS Settings

BIOS

Version 1.0.9

Version 2.0.1

Processor Settings > Logical Processors

Disabled

Disabled

Processor Settings > QPI Speed

Maximum Data Rate

Maximum Data Rate

Processor Settings > System Profile

Performance

Performance

Software and Firmware

Operating System

RHEL 6.6 x86_64

RHEL 7.2 x86_64

Intel Compiler

Version 15.0.2

Version 16.0.3

Intel MKL

Version 11.2

Version 11.3

Intel MPI

Version 5.0

Version 5.1.3

Benchmark and Applications

LINPACK

V2.1 from MKL 11.2

V2.1 from MKL 11.3

STREAM

v5.10, Array Size 1800000000, Iterations 100

v5.10, Array Size 1800000000, Iterations 100

WRF

v3.5.1, Input Data Conus12KM, Netcdf-4.3.1.1

V3.8 Input Data Conus12KM, Netcdf-4.4.0

 ANSYS Fluent  v15, Input Data: truck_poly_14m, sedan_4m, aircraft_2m  v16, Input Data: truck_poly_14m, sedan_4m, aircraft_2m

 

       Table 1: Details of Server and HPC Applications used with Broadwell-EX processors

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In this section of the blog, we have compared benchmark numbers with two generations of processors on the same server platform i.e. PowerEdge R930 as well as performance of Broadwell-EX processors with different CPU profiles and memory snoop modes namely Home Snoop (HS) and Cluster On Die(COD).

The High Performance Linpack Benchmark is a measure of a system's floating point computing power. It measures how fast a computer solves a dense n by n system of linear equations Ax = b, which is a common task in engineering. HPL benchmark was run on both PowerEdge R930 servers (With Broadwell-EX and Haswell-EX ) with block size of NB=192 and problem size of N=340992.

  

Figure 1: Comparing HPL Performance across BIOS profiles      Figure 2: Comparing HPL Performance over two generations of processors

Figure 1 depicts the performance of PowerEdge R930 server with Broadwell-EX processors on different BIOS options. HS (Home snoop mode) performs better than the COD (Cluster-on-die) on both of the system profiles Performance and DAPC. Figure 2 compares the performance between four socket Intel Xeon E7-8890 v3 and Intel Xeon E7-8890 v4 processor servers. HPL showed 47% performance improvement with four Intel Xeon E7-8890 v4 processors on R930 server in comparison to four Intel Xeon E7-8890 v3 processors. This was due to ~33% increase in the number of cores and 13% increase due to new improved version of both Intel compiler and Intel MKL.  

Stream benchmark is a synthetic benchmark program that measures sustainable memory bandwidth and the corresponding computation rate for simple vector kernels.

 

  

Figure 3: Comparing STREAM Performance across BIOS profiles   Figure 4: Comparing STREAM Performance over two generations of processors

 

As per Figure 3, the memory bandwidth of PowerEdge R930 server with Intel Broadwell-EX processors are same on different bios profiles. Figure4 shows the memory bandwidth of both Intel Xeon Broadwell-EX and Intel Xeon Haswell-EX processors with PowerEdge R930 server. Both Haswell-EX and Broadwell-EX support DDR3 and DDR4 memories respectively, while the platform with this configuration supports 1600MT/s of memory frequency for both generation of processors. Due to the same memory frequency supported by the PowerEdge R930 platform for both generation of processors, both Intel Xeon processors have same memory bandwidth of 260GB/s with the PowerEdge R930 server.

The Weather Research and Forecasting (WRF) Model is a mesoscale numerical weather prediction system designed for both atmospheric research and operational forecasting needs. It features two dynamical cores, a data assimilation system, and a software architecture facilitating parallel computation and system extensibility. The model serves a wide range of meteorological applications across scales from tens of meters to thousands of kilometers. WRF can generate atmospheric simulations using real data or idealized conditions. We used the CONUS12km and CONUS2.5km benchmark datasets for this study. CONUS12km is a single domain and small size (48hours, 12km resolution case over the Continental U.S. (CONUS) domain from October 24, 2001) benchmark with 72 seconds of time step. CONUS2.5km is a single domain and large size (Latter 3hours of a 9hours, 2.5km resolution case over the Continental U.S. (CONUS) domain from June 4, 2005) benchmark with 15 seconds of time step. WRF decomposes the domain into tasks or patches. Each patch can be further decomposed into tiles that are processed separately, but by default there is only one tile for every run. If the single tile is too large to fit into the cache of the CPU and/or core, it slows down computation due to WRF’s memory bandwidth sensitivity. In order to reduce the size of the tile, it is possible to increase the number of tiles by defining “numtile = x” in input file or defining environment variable “WRF_NUM_TILES = x”. For both CONUS 12km and CONUS 2.5km the number of tiles are chosen based on best performance which is equal to 56.

  Figure 5: Comparing WRF Performance across BIOS profiles

Figure 5 demonstrates the comparison of WRF datasets on different BIOS profiles .With Conus 12KM data ,all the bios profiles performs equally well because of the smaller data size while for CONUS 2.5KM Perf.COD (Performance System Profile with Cluster-On-Die snoop mode) gives best performance. As per the figure 5, the Cluster-on-Die snoop mode is performing 2% higher than Home snoop mode, while the Performance system profile gives 1% better performance than DAPC.

 Figure 6: Comparing WRF Performance over two generations of processors

Figure 6 shows the performance comparison between Intel Xeon Haswell-EX and Intel Xeon Broadwell-EX processors with PowerEdge R930 server. As shown in the graph, Broadwell-EX performs 24% better than Haswell-EX for CONUS 12KM data set and 6% better for CONUS 2.5KM.

ANSYS Fluent is a computational fluid dynamics (CFD) software tool. Fluent includes well-validated physical modeling capabilities to deliver fast and accurate results across the widest range of CFD and multi physics applications.

Figure 7: Comparing Fluent Performance across BIOS profiles

We used three different datasets for Fluent with ‘Solver Rating’ (Higher is better) as the performance metric. The above graph Figure 7 shows that all three datasets performed 4% better with Perf.COD (Performance System Profile with Cluster-On-Die snoop mode) bios profile than others. While, the DAPC.HS (DAPC system profile with Home snoop mode) bios profile shows lowest performance. For all three datasets ,the COD snoop mode performs 2% to 3% better than Home snoop mode and Performance system profile performs 2% to 4% better than DAPC. For all these three datasets the behaviour of Fluent is consistent.

Figure 8: Comparing Fluent Performance over two generations of processors

 

As shown above in Figure 8, for all the test cases on PowerEdge R930 with Broadwell-EX ,Fluent showed 13% to 27% performance improvement in-comparision to PowerEdge R930 with Haswell-EX.

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Conclusion:

Overall, Broadwell-EX processor makes the PowerEdge R930 server more powerful and more efficient. With Broadwell-EX, the HPL performance increses in the smae manner as increase in the number of cores in comparison to Haswell-EX. There is also increase in the performance for real time applications depending on their nature of computation. So, it can be a good choice to upgrade for those who are using compute hungry applications.