我正在尝试学习如何使用gpu进行计算或分析。我最近购买的PC配备了NVIDIA GeForce 1060 GTX和6GB内存。我已经下载并安装了NVIDIA CUDA工具包。
我正在努力熟悉gpuR。当我尝试使用像gpuMatrix（rnorm（16），nrow = 4，ncol = 4）这样的函数时，R遇到致命错误并崩溃。
我已经成功使用了使用Python 3.6.1的tensorflow-gpu，所以我知道GPU是活跃的。
代码，设备，环境和系统信息如下。 注意：操作系统是Windows 10.
我错过了什么？谢谢！
> `library(gpuR)`
Number of platforms: 1
- platform: NVIDIA Corporation: OpenCL 1.2 CUDA 8.0.0
- gpu index: 0
- GeForce GTX 1060 6GB
checked all devices
completed initialization
gpuR 1.2.3
Attaching package: ‘gpuR’
The following objects are masked from ‘package:base’:
colnames, norm, pmax, pmin, svd
> `library(devtools)`
> `gpuInfo()`
$deviceName
[1] "GeForce GTX 1060 6GB"
$deviceVendor
[1] "NVIDIA Corporation"
$numberOfCores
[1] 10
$maxWorkGroupSize
[1] 1024
$maxWorkItemDim
[1] 3
$maxWorkItemSizes
[1] 1024 1024 64
$deviceMemory
[1] 6442450944
$clockFreq
[1] 1708
$localMem
[1] 49152
$maxAllocatableMem
[1] 1610612736
$available
[1] "yes"
$deviceExtensions
[1] "cl_khr_global_int32_base_atomics" "cl_khr_global_int32_extended_atomics" "cl_khr_local_int32_base_atomics"
[4] "cl_khr_local_int32_extended_atomics" "cl_khr_fp64" "cl_khr_byte_addressable_store"
[7] "cl_khr_icd" "cl_khr_gl_sharing" "cl_nv_compiler_options"
[10] "cl_nv_device_attribute_query" "cl_nv_pragma_unroll" "cl_nv_d3d10_sharing"
[13] "cl_khr_d3d10_sharing" "cl_nv_d3d11_sharing" "cl_nv_copy_opts"
$double_support
[1] TRUE
> `currentDevice()`
$device
[1] "GeForce GTX 1060 6GB"
$device_index
[1] 1
$device_type
[1] "gpu"
> `currentPlatform()`
$platform
[1] "NVIDIA Corporation: OpenCL 1.2 CUDA 8.0.0"
$platform_index
[1] 1
> `devtools::session_info()`
Session info ------------------------------------------------------------------------------------------------------------
setting value
version R version 3.4.1 (2017-06-30)
system x86_64, mingw32
ui RStudio (1.0.153)
language (EN)
collate English_United States.1252
tz America/Phoenix
date 2017-08-13
Packages ----------------------------------------------------------------------------------------------------------------
package * version date source
assertive 0.3-5 2016-12-31 CRAN (R 3.4.1)
assertive.base 0.0-7 2016-12-30 CRAN (R 3.4.1)
assertive.code 0.0-1 2015-10-06 CRAN (R 3.4.1)
assertive.data 0.0-1 2015-10-06 CRAN (R 3.4.1)
assertive.data.uk 0.0-1 2015-10-06 CRAN (R 3.4.1)
assertive.data.us 0.0-1 2015-10-06 CRAN (R 3.4.1)
assertive.datetimes 0.0-2 2016-05-10 CRAN (R 3.4.1)
assertive.files 0.0-2 2016-05-10 CRAN (R 3.4.1)
assertive.matrices 0.0-1 2015-10-06 CRAN (R 3.4.1)
assertive.models 0.0-1 2015-10-06 CRAN (R 3.4.1)
assertive.numbers 0.0-2 2016-05-09 CRAN (R 3.4.1)
assertive.properties 0.0-4 2016-12-30 CRAN (R 3.4.1)
assertive.reflection 0.0-4 2016-12-30 CRAN (R 3.4.1)
assertive.sets 0.0-3 2016-12-30 CRAN (R 3.4.1)
assertive.strings 0.0-3 2016-05-10 CRAN (R 3.4.1)
assertive.types 0.0-3 2016-12-30 CRAN (R 3.4.1)
base * 3.4.1 2017-06-30 local
codetools 0.2-15 2016-10-05 CRAN (R 3.4.1)
compiler 3.4.1 2017-06-30 local
datasets * 3.4.1 2017-06-30 local
devtools * 1.13.3 2017-08-02 CRAN (R 3.4.1)
digest 0.6.12 2017-01-27 CRAN (R 3.4.1)
gpuR * 1.2.3 2017-08-13 Github (cdeterman/gpuR@de6d377)
graphics * 3.4.1 2017-06-30 local
grDevices * 3.4.1 2017-06-30 local
knitr 1.17 2017-08-10 CRAN (R 3.4.1)
memoise 1.1.0 2017-04-21 CRAN (R 3.4.1)
methods * 3.4.1 2017-06-30 local
Rcpp 0.12.12 2017-07-15 CRAN (R 3.4.1)
stats * 3.4.1 2017-06-30 local
stringi 1.1.5 2017-04-07 CRAN (R 3.4.0)
tools 3.4.1 2017-06-30 local
utils * 3.4.1 2017-06-30 local
withr 2.0.0 2017-07-28 CRAN (R 3.4.1)