在ESXI添加硬盘时提示 无法创建 VMFS 数据存储 esxi7.0无法引导

淘宝买了一块 Tesla K80，1300多大洋，只因硬件参数挺诱人。我简单说一下使用这张卡的一点挫折，希望对想买的同学有帮助。

电源要求额定700w以上，要不然带不动，我最开始用的一台dell工作站，635w额定电源，点不亮！

这块卡发热量挺大，且没有风扇，适合放服务器，散热会好些

这块卡自带双路，可以直通到 2 台虚拟机中，官方说法是双GPU设计

官方资料 https://www.nvidia.com/en-gb/data-center/tesla-k80/

按常规设置新建虚拟机，添加PCI设备，如下图

坑1：无法打开虚拟机 CentOS 7 K80 的电源。内存设置无效: 内存预留 (sched.mem.min) 应该等于内存大小 (16384)。 单击此处了解更多详细信息。

解决方法：打勾☑️ 预留所有客户机内存（全部锁定）即可

 

坑2：无法打开虚拟机 CentOS 7 K80 的电源。模块“DevicePowerOn”打开电源失败。 单击此处了解更多详细信息。

这个问题折腾了我一阵子，有几个地方要改：

1. 物理机 (Dell R710) BIOS 中启用大于4G的内存映射 (Memory Mapped IO >4GB)，这里就不弄图了，搜一下吧
2. 编辑虚拟机 - 虚拟机选项 - 引导选项 - 固件 - EFI
3. 编辑虚拟机 - 虚拟机选项 - 高级 - 配置参数 - 编辑配置... -- 点2次左上角的 [添加参数]，此时会在列表的尾部会出现 2 条显示为 “单击以编辑键” 条目，如图：
4. 配置 pciPassthru.use64bitMMIO 健值为 TRUE
5. 配置 pciPassthru.64bitMMIOSizeGB 键值为 48 ，这里的数值需要用GPU内存进行计算，我大致理解为 直通GPU的内存乘以四，我的卡为 双路12G ，映射一路，12*4 = 48，所以这里我写了48，如果理解有误，请大家联系我更正。
6. 以上配置参考来源于 https://octo.vmware.com/enable-compute-accelerators-vsphere-6-5-machine-learning-hpc-workloads/ 
7. 配好后像这样

坑3（2021.02.02 遇上）：配置文件中缺少 pciPassthru0.id 条目。

虚拟机被删除、重新注册、恢复快照之后遇上此问题。解决办法是：编辑虚拟机 -> 删除直通设备 -> 保存 -> 编辑虚拟机->添加直通设备->保存！删掉后一定要先保存，如果删掉后马上添加，此问题继续存在！

经过以上配置，成功配置Nvidia Tesla K80 的直通

 

单路 GPU 跑 CUDA 的两个示例程序，结果如下

[root@K80-01 deviceQuery]# ./deviceQuery 
./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "Tesla K80"
  CUDA Driver Version / Runtime Version          10.2 / 10.2
  CUDA Capability Major/Minor version number:    3.7
  Total amount of global memory:                 11441 MBytes (11996954624 bytes)
  (13) Multiprocessors, (192) CUDA Cores/MP:     2496 CUDA Cores
  GPU Max Clock rate:                            824 MHz (0.82 GHz)
  Memory Clock rate:                             2505 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
  Device supports Compute Preemption:            No
  Supports Cooperative Kernel Launch:            No
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 11 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1
Result = PASS
[root@K80-01 deviceQuery]# cd ..
[root@K80-01 1_Utilities]# cd bandwidthTest/
[root@K80-01 bandwidthTest]# ls
bandwidthTest.cu  Makefile  NsightEclipse.xml  readme.txt
[root@K80-01 bandwidthTest]# make
/usr/local/cuda/bin/nvcc -ccbin g++ -I../../common/inc  -m64    -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75 -o bandwidthTest.o -c bandwidthTest.cu
/usr/local/cuda/bin/nvcc -ccbin g++   -m64      -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75 -o bandwidthTest bandwidthTest.o 
mkdir -p ../../bin/x86_64/linux/release
cp bandwidthTest ../../bin/x86_64/linux/release
[root@K80-01 bandwidthTest]# 
[root@K80-01 bandwidthTest]# ./bandwidthTest 
[CUDA Bandwidth Test] - Starting...
Running on...

 Device 0: Tesla K80
 Quick Mode

 Host to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)	Bandwidth(GB/s)
   32000000			9.9

 Device to Host Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)	Bandwidth(GB/s)
   32000000			11.3

 Device to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)	Bandwidth(GB/s)
   32000000			157.8

Result = PASS

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.