Dear users and developers,
Currently I am using two Tesla K40m cards for my computational work on
quantum espresso (QE) suit http://www.quantum-espresso.org/. My GPU enabled
QE code running very slower than normal version. When I am submitting my
job on gpu it was showing some error that "A high-performance Open MPI
point-to-point messaging module was unable to find any relevant network
interfaces:
Module: OpenFabrics (openib)
Host: qmel
Another transport will be used instead, although this may result in
lower performance.
Is this the reason for diminishing GPU performance ??
I done installation by
1. ./configure --prefix=/home/xxxx/software/openmpi-2.0.4
--disable-openib-dynamic-sl --disable-openib-udcm --disable-openib-rdmacm"
because we don't have any Infiband adapter HCA in server.
2. make all
3. make install
Please correct me If I done any mistake in my installation or I have to use
Infiband adaptor for using Openmpi??
I read lot of posts in openmpi forum to remove above error while submitting
job, I added tag of "--mca btl ^openib" , still no use error vanished but
performance was same.
Current details of server are:
Server: FUJITSU PRIMERGY RX2540 M2
CUDA version: 9.0
openmpi version: 2.0.4 with intel mkl libraries
QE-gpu version (my application): 5.4.0
P.S: Extra information attached
Thanks in advance
Regards
Phanikumar
Research scholar
IIT Kharagpur
Kharagpur, westbengal
India
##################################################################################################################################################
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
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