[Pw_forum] Pw_forum Digest, Vol 125, Issue 8

Phanikumar Pentyala phani12.chem at gmail.com
Mon Dec 11 03:53:52 CET 2017


​Thank you Rolly for your comments

Previously I used both intel MKL and MPI. MPI (intel) was not running at
all so that I switched to Openmpi. current version of my intel MKL library
was "l_mkl_2018.1.163"

My linux-OS was Ubuntu-16.04 serever, Is OS also create some problem??

Can you explain Is there any difference between Parallel Studio XE inetel
and above intel MKL (above version)??


(sorry , since it was so long time using pw-forum so I forgot that, This is
my affiliation)

​Phanikumar
Research scholar
Department of Chemical engineering
Indian Institute of Technology Kharagpur
​West Bengal
India



> Message: 4
> Date: Sun, 10 Dec 2017 09:01:59 +0530
> From: Phanikumar Pentyala <phani12.chem at gmail.com>
> Subject: [Pw_forum] QE-GPU performance
> To: PWSCF Forum <pw_forum at pwscf.org>
> Message-ID:
>         <CAOgLYHHDQWV7JeYe17KBTwGwv4NVyNTJ-6XpqKfkVjXYbj8ELQ at mail.
> gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Dear users and developers
>
> Currently I am using two Tesla K40m cards for my computational work on
> quantum espresso (QE). My GPU enabled QE code running very slower than
> normal version. My question was weather particular application will be fast
> only in some versions of CUDA toolkit? (as mentioned in previous post:
> http://qe-forge.org/pipermail/pw_forum/2015-May/106889.html) OR is there
> any other reason hindering performance (memory) of GPU? (when I am hitting
> top command in my server, option of 'VIRT' showing different values (top
> command pasted in attached file))
>
> Some error was generating while submitting code that "A high-performance
> Open MPI point-to-point messaging module was unable to find any relevant
> network interfaces: Module: OpenFabrics (openib)  Host: XXXX Another
> transport will be used instead, although this may result in lower
> performance".  Is this MPI thread hindering GPU performance ?
>
> (P.S: We don't have any Infiband adapter HCA in server)
>
>
> Current details of server are (full details attached):
>
> Server: FUJITSU PRIMERGY RX2540 M2
> CUDA version: 9.0
> NVIDIA driver: 384.9
> openmpi version: 2.0.4 with intel mkl libraries
> QE-gpu version : 5.4.0
>
>
> Thanks in advance
>
> Regards
> Phanikumar
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> ############################################################
> ############################################################
> ##########################
>
> SERVER architecture information (from "lscpu" command in terminal)
>
> ############################################################
> ############################################################
> ##########################
>
> Architecture:          x86_64
> CPU op-mode(s):        32-bit, 64-bit
> Byte Order:            Little Endian
> CPU(s):                40
> On-line CPU(s) list:   0-39
> Thread(s) per core:    2
> Core(s) per socket:    10
> Socket(s):             2
> NUMA node(s):          2
> Vendor ID:             GenuineIntel
> CPU family:            6
> Model:                 79
> Model name:            Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz
> Stepping:              1
> CPU MHz:               1200.000
> BogoMIPS:              4788.53
> Virtualization:        VT-x
> L1d cache:             32K
> L1i cache:             32K
> L2 cache:              256K
> L3 cache:              25600K
> NUMA node0 CPU(s):     0-9,20-29
> NUMA node1 CPU(s):     10-19,30-39
>
>
> ############################################################
> ############################################################
> ##########################
>
> After I run device quiry in CUDA_samples I got this information about my
> GPU accelerators
>
> ############################################################
> ############################################################
> ##########################
>
>  CUDA Device Query (Runtime API) version (CUDART static linking)
>
> Detected 2 CUDA Capable device(s)
>
> Device 0: "Tesla K40m"
>   CUDA Driver Version / Runtime Version          9.0 / 9.0
>   CUDA Capability Major/Minor version number:    3.5
>   Total amount of global memory:                 11440 MBytes (11995578368
> bytes)
>   (15) Multiprocessors, (192) CUDA Cores/MP:     2880 CUDA Cores
>   GPU Max Clock rate:                            745 MHz (0.75 GHz)
>   Memory Clock rate:                             3004 Mhz
>   Memory Bus Width:                              384-bit
>   L2 Cache Size:                                 1572864 bytes
>   Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536,
> 65536), 3D=(4096, 4096, 4096)
>   Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
>   Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048
> layers
>   Total amount of constant memory:               65536 bytes
>   Total amount of shared memory per block:       49152 bytes
>   Total number of registers available per block: 65536
>   Warp size:                                     32
>   Maximum number of threads per multiprocessor:  2048
>   Maximum number of threads per block:           1024
>   Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
>   Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
>   Maximum memory pitch:                          2147483647 bytes
>   Texture alignment:                             512 bytes
>   Concurrent copy and kernel execution:          Yes with 2 copy engine(s)
>   Run time limit on kernels:                     No
>   Integrated GPU sharing Host Memory:            No
>   Support host page-locked memory mapping:       Yes
>   Alignment requirement for Surfaces:            Yes
>   Device has ECC support:                        Enabled
>   Device supports Unified Addressing (UVA):      Yes
>   Supports Cooperative Kernel Launch:            No
>   Supports MultiDevice Co-op Kernel Launch:      No
>   Device PCI Domain ID / Bus ID / location ID:   0 / 2 / 0
>   Compute Mode:
>      < Default (multiple host threads can use ::cudaSetDevice() with
> device simultaneously) >
>
> Device 1: "Tesla K40m"
>   CUDA Driver Version / Runtime Version          9.0 / 9.0
>   CUDA Capability Major/Minor version number:    3.5
>   Total amount of global memory:                 11440 MBytes (11995578368
> bytes)
>   (15) Multiprocessors, (192) CUDA Cores/MP:     2880 CUDA Cores
>   GPU Max Clock rate:                            745 MHz (0.75 GHz)
>   Memory Clock rate:                             3004 Mhz
>   Memory Bus Width:                              384-bit
>   L2 Cache Size:                                 1572864 bytes
>   Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536,
> 65536), 3D=(4096, 4096, 4096)
>   Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
>   Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048
> layers
>   Total amount of constant memory:               65536 bytes
>   Total amount of shared memory per block:       49152 bytes
>   Total number of registers available per block: 65536
>   Warp size:                                     32
>   Maximum number of threads per multiprocessor:  2048
>   Maximum number of threads per block:           1024
>   Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
>   Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
>   Maximum memory pitch:                          2147483647 bytes
>   Texture alignment:                             512 bytes
>   Concurrent copy and kernel execution:          Yes with 2 copy engine(s)
>   Run time limit on kernels:                     No
>   Integrated GPU sharing Host Memory:            No
>   Support host page-locked memory mapping:       Yes
>   Alignment requirement for Surfaces:            Yes
>   Device has ECC support:                        Enabled
>   Device supports Unified Addressing (UVA):      Yes
>   Supports Cooperative Kernel Launch:            No
>   Supports MultiDevice Co-op Kernel Launch:      No
>   Device PCI Domain ID / Bus ID / location ID:   0 / 129 / 0
>   Compute Mode:
>      < Default (multiple host threads can use ::cudaSetDevice() with
> device simultaneously) >
> > Peer access from Tesla K40m (GPU0) -> Tesla K40m (GPU1) : No
> > Peer access from Tesla K40m (GPU1) -> Tesla K40m (GPU0) : No
>
> deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.0, CUDA Runtime
> Version = 9.0, NumDevs = 2
> Result = PASS
>
>
> ############################################################
> ############################################################
> ##########################
>
> GPU performance after 'nvidia-smi' command in terminal
>
> ############################################################
> ############################################################
> ##########################
>
> +-----------------------------------------------------------
> ------------------+
> | NVIDIA-SMI 384.90                 Driver Version: 384.90
>     |
> |-------------------------------+----------------------+----
> ------------------+
> | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr.
> ECC |
> | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute
> M. |
> |===============================+======================+====
> ==================|
> |   0  Tesla K40m          Off  | 00000000:02:00.0 Off |
>   0 |
> | N/A   42C    P0    75W / 235W |  11381MiB / 11439MiB |     83%
> Default |
> +-------------------------------+----------------------+----
> ------------------+
> |   1  Tesla K40m          Off  | 00000000:81:00.0 Off |
>   0 |
> | N/A   46C    P0    75W / 235W |  11380MiB / 11439MiB |     87%
> Default |
> +-------------------------------+----------------------+----
> ------------------+
>
>
> ############################################################
> ############################################################
> ##########################
>
> TOP command if my server
>
> ############################################################
> ############################################################
> ##########################
> PID   USER      PR  NI   VIRT    RES   SHR   S %CPU  %MEM     TIME+ COMMAND
> 20019 xxxxx     20   0  0.158t 426080 152952 R 100.3  0.3  36:29.44
> pw-gpu.x
> 20023 xxxxx     20   0  0.158t 422380 153328 R 100.0  0.3  36:29.42
> pw-gpu.x
> 20025 xxxxx     20   0  0.158t 418256 153376 R 100.0  0.3  36:27.74
> pw-gpu.x
> 20042 xxxxx     20   0  0.158t 416912 153104 R 100.0  0.3  36:24.63
> pw-gpu.x
> 20050 xxxxx     20   0  0.158t 412564 153084 R 100.0  0.3  36:25.68
> pw-gpu.x
> 20064 xxxxx     20   0  0.158t 408012 153100 R 100.0  0.3  36:25.54
> pw-gpu.x
> 20098 xxxxx     20   0  0.158t 398404 153436 R 100.0  0.3  36:27.92
> pw-gpu.x
>
>
> ------------------------------
>
> Message: 5
> Date: Sun, 10 Dec 2017 17:07:59 +0800
> From: Rolly Ng <rollyng at gmail.com>
> Subject: Re: [Pw_forum] QE-GPU performance
> To: pw_forum at pwscf.org
> Message-ID: <225411b4-1c48-6f24-954f-5d0af115e76f at gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Dear Phanikumar,
>
> Please include your affiliation when posting to the forum.
>
> In my experience with QE-GPU v5.3.0 and v5.4.0, the working combination
> of software is,
>
> 1) Intel PSXE 2017
>
> 2) CUDA 6.5 or 7.0
>
> 3) Centos 7.1
>
> Please try the above combination.
>
> Regards,
> Rolly
>
> PhD. Research Fellow,
> Dept. of Physics & Materials Science,
> City University of Hong Kong
> Tel: +852 3442 4000
> Fax: +852 3442 0538
>
> On 12/10/2017 11:31 AM, Phanikumar Pentyala wrote:
> > Dear users and developers
> >
> > Currently I am using two Tesla K40m cards for my computational work on
> > quantum espresso (QE). My GPU enabled QE code running very slower than
> > normal version. My question was weather particular application will be
> > fast only in some versions of CUDA toolkit? (as mentioned in previous
> > post: http://qe-forge.org/pipermail/pw_forum/2015-May/106889.html) OR
> > is there any other reason hindering performance (memory) of GPU? (when
> > I am hitting top command in my server, option of 'VIRT' showing
> > different values (top command pasted in attached file))
> >
> > Some error was generating while submitting code that "A
> > high-performance Open MPI point-to-point messaging module was unable
> > to find any relevant network interfaces: Module: OpenFabrics (openib)?
> > Host: XXXX Another transport will be used instead, although this may
> > result in lower performance". Is this MPI thread hindering GPU
> > performance ?
> >
> > (P.S: We don't have any Infiband adapter HCA in server)
> >
> >
> > Current details of server are (full details attached):
> >
> > Server: FUJITSU PRIMERGY RX2540 M2
> > CUDA version: 9.0
> > NVIDIA driver: 384.9
> > openmpi version: 2.0.4 with intel mkl libraries
> > QE-gpu version : 5.4.0
> >
> >
> > Thanks in advance
> >
> > Regards
> > Phanikumar
> >
> >
> > _______________________________________________
> > Pw_forum mailing list
> > Pw_forum at pwscf.org
> > http://pwscf.org/mailman/listinfo/pw_forum
>
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> End of Pw_forum Digest, Vol 125, Issue 8
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>
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