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re-test of gpuR
R version 3.4.3 (2017-11-30) -- "Kite-Eating Tree"
Copyright (C) 2017 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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> devtools::test()
Loading gpuR
Loading required package: testthat
Creating a generic function for ‘eigen’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘%o%’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘nrow’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘ncol’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘crossprod’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘tcrossprod’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘cov’ from package ‘stats’ in package ‘gpuR’
Creating a generic function for ‘colSums’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘rowSums’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘colMeans’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘rowMeans’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘dist’ from package ‘stats’ in package ‘gpuR’
Creating a generic function for ‘diag’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘diag<-’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘det’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘norm’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘qr.R’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘qr.Q’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘typeof’ from package ‘base’ in package ‘gpuR’
Creating a generic function for ‘colnames<-’ from package ‘base’ in package ‘gpuR’
Number of platforms: 2
- platform: Advanced Micro Devices, Inc.: OpenCL 2.0 AMD-APP (2482.3)
- context device index: 0
- Bonaire
- platform: The pocl project: OpenCL 1.2 pocl 1.1-pre, LLVM 5.0.0
- context device index: 0
- pthread-AMD FX(tm)-8350 Eight-Core Processor
checked all devices
completed initialization
gpuR 2.0.2
Testing gpuR
CPU deepcopy: ....................................................
CPU gpuMatrix algebra: ................................................................................................................................................................................................................................................
CPU gpuMatrix chol decomposition: ......
CPU gpuMatrix classes: .........................................................
CPU gpuMatrix Correlations: ....
CPU gpuMatrix Distance Computations: ..........................
CPU gpuMatrix eigen decomposition: ......
CPU gpuMatrix math operations: ..................................................................
CPU gpuMatrix norm: ..................
CPU gpuMatrix qr decomposition: ........
CPU gpuMatrix Row and Column Methods: ........................................................
CPU gpuMatrix solve: ........................
CPU gpuMatrix svd decomposition: ..........
CPU gpuMatrix Utility Functions: ............................................................
CPU gpuMatrixBlock algebra: ..........................................................................................
CPU gpuVector algebra: ..........................................................................................
CPU gpuVector classes: .................................
CPU gpuVector math operations: ..................................................................
CPU gpuVector Utility Functions: ...................
CPU Inplace Algebra Operations: ................................................................................................
CPU Inplace Math Operations: ............................................................................
CPU Ordering Methods: ....
CPU vclVector shared memory: ....
CPU vclMatrix algebra: ...................................................................................................................................................................................................................................................
CPU vclMatrix chol decomposition: ......
CPU vclMatrix classes: ....................................................
CPU vclMatrix Correlations: ......
CPU vclMatrix Distance Computations: ..........................
CPU vclMatrix eigen decomposition: ......
CPU vclMatrix norm: ..................
CPU vclMatrix qr decomposition: ........
CPU vclMatrix Row and Column Methods: ........................................................
CPU vclMatrix solve: ........................
CPU vclMatrix svd decomposition: ..........
CPU vclMatrixBlock algebra: ..........................................................................................
CPU vclMatrix math operations: ..................................................................
CPU vclMatrix Utility Functions: ..............................................................
CPU vclVector algebra: ..................................................................................
CPU vclVector classes: ................................
CPU vclVector math operations: ..................................................................
CPU vclVector Utility Functions: .......................
Custom OpenCL: In file included from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/backend.hpp:26:0,
from file3c902b486987.cpp:8:
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/context.hpp: In member function ‘void viennacl::ocl::context::add_queue(cl_device_id)’:
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/context.hpp:262:93: warning: ‘_cl_command_queue* clCreateCommandQueue(cl_context, cl_device_id, cl_command_queue_properties, cl_int*)’ is deprecated [-Wdeprecated-declarations]
viennacl::ocl::handle<cl_command_queue> temp(clCreateCommandQueue(h_.get(), dev, 0, &err), *this);
^
In file included from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/context.hpp:28:0,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/backend.hpp:26,
from file3c902b486987.cpp:8:
/data/Installers/gpuR/include/CL/cl.h:1427:1: note: declared here
clCreateCommandQueue(cl_context /* context */,
^~~~~~~~~~~~~~~~~~~~
In file included from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/linalg/scalar_operations.hpp:27:0,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/scalar.hpp:30,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/tools/entry_proxy.hpp:27,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/detail/matrix_def.hpp:26,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/matrix.hpp:26,
from /data/Installers/gpuR/include/gpuR/dynVCLMat.hpp:17,
from /data/Installers/gpuR/include/gpuR/getVCLptr.hpp:5,
from file3c902b486987.cpp:11:
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/meta/predicate.hpp: At global scope:
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/meta/predicate.hpp:513:38: warning: ignoring attributes on template argument ‘cl_float {aka float}’ [-Wignored-attributes]
template<> struct is_cl_type<cl_float> { enum { value = true }; };
^
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/meta/predicate.hpp:514:39: warning: ignoring attributes on template argument ‘cl_double {aka double}’ [-Wignored-attributes]
template<> struct is_cl_type<cl_double>{ enum { value = true }; };
^
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/meta/predicate.hpp:515:37: warning: ignoring attributes on template argument ‘cl_uint {aka unsigned int}’ [-Wignored-attributes]
template<> struct is_cl_type<cl_uint> { enum { value = true }; };
^
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/meta/predicate.hpp:516:36: warning: ignoring attributes on template argument ‘cl_int {aka int}’ [-Wignored-attributes]
template<> struct is_cl_type<cl_int> { enum { value = true }; };
^
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/meta/predicate.hpp:519:38: warning: ignoring attributes on template argument ‘cl_ulong {aka long unsigned int}’ [-Wignored-attributes]
template<> struct is_cl_type<cl_ulong> { enum { value = true }; };
^
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/meta/predicate.hpp:520:37: warning: ignoring attributes on template argument ‘cl_long {aka long int}’ [-Wignored-attributes]
template<> struct is_cl_type<cl_long> { enum { value = true }; };
^
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/meta/predicate.hpp:521:39: warning: ignoring attributes on template argument ‘cl_ushort {aka short unsigned int}’ [-Wignored-attributes]
template<> struct is_cl_type<cl_ushort>{ enum { value = true }; };
^
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/meta/predicate.hpp:522:38: warning: ignoring attributes on template argument ‘cl_short {aka short int}’ [-Wignored-attributes]
template<> struct is_cl_type<cl_short> { enum { value = true }; };
^
In file included from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/device_specific/builtin_database/devices/gpu/amd/ni/barts.hpp:24:0,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/device_specific/builtin_database/matrix_product.hpp:21,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/linalg/opencl/kernels/matrix.hpp:28,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/linalg/opencl/matrix_operations.hpp:46,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/linalg/matrix_operations.hpp:41,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/matrix.hpp:28,
from /data/Installers/gpuR/include/gpuR/dynVCLMat.hpp:17,
from /data/Installers/gpuR/include/gpuR/getVCLptr.hpp:5,
from file3c902b486987.cpp:11:
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/device_specific/builtin_database/common.hpp:54:76: warning: ignoring attributes on template argument ‘viennacl::device_specific::device_type {aka long unsigned int}’ [-Wignored-attributes]
struct device_type_t{ typedef std::map<device_type, device_architecture_t> map_t; map_t d; };
^
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/device_specific/builtin_database/common.hpp:55:62: warning: ignoring attributes on template argument ‘viennacl::device_specific::vendor_id_type {aka unsigned int}’ [-Wignored-attributes]
struct type{ typedef std::map<vendor_id_type, device_type_t> map_t; map_t d; };
^
In file included from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/backend.hpp:27:0,
from file3c902b486987.cpp:8:
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/enqueue.hpp: In instantiation of ‘void viennacl::ocl::enqueue(KernelType&, const viennacl::ocl::command_queue&) [with KernelType = viennacl::ocl::kernel]’:
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/enqueue.hpp:134:10: required from ‘void viennacl::ocl::enqueue(KernelType&) [with KernelType = viennacl::ocl::kernel]’
file3c902b486987.cpp:133:108: required from here
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/enqueue.hpp:74:26: warning: ‘cl_int clEnqueueTask(cl_command_queue, cl_kernel, cl_uint, _cl_event* const*, _cl_event**)’ is deprecated [-Wdeprecated-declarations]
err = clEnqueueTask(queue.handle().get(), k.handle().get(), 0, NULL, NULL);
~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In file included from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/context.hpp:28:0,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/backend.hpp:26,
from file3c902b486987.cpp:8:
/data/Installers/gpuR/include/CL/cl.h:1441:1: note: declared here
clEnqueueTask(cl_command_queue /* command_queue */,
^~~~~~~~~~~~~
In file included from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/backend.hpp:27:0,
from file3c902b486987.cpp:8:
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/enqueue.hpp:74:26: warning: ‘cl_int clEnqueueTask(cl_command_queue, cl_kernel, cl_uint, _cl_event* const*, _cl_event**)’ is deprecated [-Wdeprecated-declarations]
err = clEnqueueTask(queue.handle().get(), k.handle().get(), 0, NULL, NULL);
~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In file included from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/context.hpp:28:0,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/backend.hpp:26,
from file3c902b486987.cpp:8:
/data/Installers/gpuR/include/CL/cl.h:1441:1: note: declared here
clEnqueueTask(cl_command_queue /* command_queue */,
^~~~~~~~~~~~~
In file included from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/backend.hpp:27:0,
from file3c902b486987.cpp:8:
/home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/enqueue.hpp:74:26: warning: ‘cl_int clEnqueueTask(cl_command_queue, cl_kernel, cl_uint, _cl_event* const*, _cl_event**)’ is deprecated [-Wdeprecated-declarations]
err = clEnqueueTask(queue.handle().get(), k.handle().get(), 0, NULL, NULL);
~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In file included from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/context.hpp:28:0,
from /home/znmeb/R/x86_64-pc-linux-gnu-library/3.4/RViennaCL/include/viennacl/ocl/backend.hpp:26,
from file3c902b486987.cpp:8:
/data/Installers/gpuR/include/CL/cl.h:1441:1: note: declared here
clEnqueueTask(cl_command_queue /* command_queue */,
^~~~~~~~~~~~~
.
deepcopy: .......................................................
gpuMatrix algebra: ................................................................................................................................................................................................................................................
gpuMatrix chol decomposition: ......
gpuMatrix classes: ...........................................................................
gpuMatrix Correlations: ......
gpuMatrix Distance Computations: ..........................
gpuMatrix eigen decomposition: ......
gpuMatrix math operations: ..................................................................
gpuMatrix norm: ..................
gpuMatrix qr decomposition: ........
gpuMatrix Row and Column Methods: ..........................................................
gpuMatrix solve: ........................
gpuMatrix svd decomposition: ..........
gpuMatrix Utility Functions: ...................................
gpuMatrixBlock algebra: ..........................................................................................
gpuVector algebra: ..........................................................................................
gpuVector classes: .................................
gpuVector math operations: .........................1........................................
gpuVector Utility Functions: ...................
Inplace Operations: ........................
Check Internal C++ Errors: ............................................................................................................................
Ordering Methods: ........
vclVector shared memory: ....2
Switching GPUs vclMatrix algebra: SSSSSSSSSSSSSSSdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
Sdevice found:
94439691514881
S
Switching GPUs vclMatrix classes: SSSSSSSSS
Switching GPUs vclMatrix Correlations: SS
Switching GPU vclMatrix Distance Computations: SSSSSSSS
Switching GPU vclMatrix eigen decomposition: SS
Switching GPU vclMatrix math operations: SSSSSSSSSSSSSS
Switching GPU vclMatrix Row and Column Methods: SSSSSSSSSSSSSSSSSSSS
Switching GPU vclMatrix Utility Functions: SSSS
Switching GPU vclMatrixBlock algebra: SSSSSSSSSSSSSSSSSSSSSSSSSSSS
Utility Functions: ...........................
vclMatrix algebra: ....................................................................................................................................................................................................................................................
vclMatrix chol decomposition: ......
vclMatrix classes: ....................................................
vclMatrix Correlations: ......
vclMatrix Distance Computations: ..........................
vclMatrix eigen decomposition: ........
vclMatrix math operations: ..................................................................
vclMatrix norm: ..................
vclMatrix qr decomposition: ........
vclMatrix Row and Column Methods: ..........................................................
vclMatrix solve: ........................
vclMatrix svd decomposition: ..........
vclMatrix Utility Functions: ..............................................................
vclMatrixBlock algebra: ..........................................................................................
vclVector algebra: ..................................................................................
vclVector classes: ................................
vclVector math operations: .........................3........................................
vclVector Utility Functions: .......................
Skipped ------------------------------------------------------------------------
1. Switching GPUs vclMatrix Single Precision Matrix Multiplication (@test_switch_gpu_vclMatrix_algebra.R#26) - Only one GPU available
2. Switching GPUs vclMatrix Single Precision Matrix Subtraction (@test_switch_gpu_vclMatrix_algebra.R#53) - Only one GPU available
3. Switching GPUs vclMatrix Single Precision Scalar Matrix Subtraction (@test_switch_gpu_vclMatrix_algebra.R#80) - Only one GPU available
4. Switching GPUs vclMatrix Single Precision Unary Scalar Matrix Subtraction (@test_switch_gpu_vclMatrix_algebra.R#113) - Only one GPU available
5. Switching GPUs vclMatrix Single Precision Matrix Addition (@test_switch_gpu_vclMatrix_algebra.R#139) - Only one GPU available
6. Switching GPUs vclMatrix Single Precision Scalar Matrix Addition (@test_switch_gpu_vclMatrix_algebra.R#166) - Only one GPU available
7. Switching GPUs vclMatrix Single Precision Matrix Element-Wise Multiplication (@test_switch_gpu_vclMatrix_algebra.R#199) - Only one GPU available
8. Switching GPUs vclMatrix Single Precision Scalar Matrix Multiplication (@test_switch_gpu_vclMatrix_algebra.R#226) - Only one GPU available
9. Switching GPUs vclMatrix Single Precision Matrix Element-Wise Division (@test_switch_gpu_vclMatrix_algebra.R#259) - Only one GPU available
10. Switching GPUs vclMatrix Single Precision Scalar Matrix Division (@test_switch_gpu_vclMatrix_algebra.R#286) - Only one GPU available
11. Switching GPUs vclMatrix Single Precision Matrix Element-Wise Power (@test_switch_gpu_vclMatrix_algebra.R#319) - Only one GPU available
12. Switching GPUs vclMatrix Single Precision Scalar Matrix Power (@test_switch_gpu_vclMatrix_algebra.R#346) - Only one GPU available
13. Switching GPUs vclMatrix Single Precision crossprod (@test_switch_gpu_vclMatrix_algebra.R#372) - Only one GPU available
14. Switching GPUs vclMatrix Single Precision tcrossprod (@test_switch_gpu_vclMatrix_algebra.R#411) - Only one GPU available
15. Switching GPUs vclMatrix Single Precision transpose (@test_switch_gpu_vclMatrix_algebra.R#451) - Only one GPU available
16. Switching GPUs vclMatrix Double Precision Matrix Multiplication (@test_switch_gpu_vclMatrix_algebra.R#474) - Less than 2 GPUs with double precision
17. Switching GPUs vclMatrix Double Precision Matrix Subtraction (@test_switch_gpu_vclMatrix_algebra.R#501) - Less than 2 GPUs with double precision
18. Switching GPUs vclMatrix Double Precision Scalar Matrix Subtraction (@test_switch_gpu_vclMatrix_algebra.R#528) - Less than 2 GPUs with double precision
19. Switching GPUs vclMatrix Double Precision Unary Scalar Matrix Subtraction (@test_switch_gpu_vclMatrix_algebra.R#561) - Less than 2 GPUs with double precision
20. Switching GPUs vclMatrix Double Precision Matrix Addition (@test_switch_gpu_vclMatrix_algebra.R#587) - Less than 2 GPUs with double precision
21. Switching GPUs vclMatrix Double Precision Scalar Matrix Addition (@test_switch_gpu_vclMatrix_algebra.R#614) - Less than 2 GPUs with double precision
22. Switching GPUs vclMatrix Double Precision Matrix Element-Wise Multiplication (@test_switch_gpu_vclMatrix_algebra.R#647) - Less than 2 GPUs with double precision
23. Switching GPUs vclMatrix Double Precision Scalar Matrix Multiplication (@test_switch_gpu_vclMatrix_algebra.R#674) - Less than 2 GPUs with double precision
24. Switching GPUs vclMatrix Double Precision Matrix Element-Wise Division (@test_switch_gpu_vclMatrix_algebra.R#707) - Less than 2 GPUs with double precision
25. Switching GPUs vclMatrix Double Precision Scalar Matrix Division (@test_switch_gpu_vclMatrix_algebra.R#734) - Less than 2 GPUs with double precision
26. Switching GPUs vclMatrix Double Precision Matrix Element-Wise Power (@test_switch_gpu_vclMatrix_algebra.R#767) - Less than 2 GPUs with double precision
27. Switching GPUs vclMatrix Double Precision Scalar Matrix Power (@test_switch_gpu_vclMatrix_algebra.R#794) - Less than 2 GPUs with double precision
28. Switching GPUs vclMatrix Double Precision crossprod (@test_switch_gpu_vclMatrix_algebra.R#820) - Less than 2 GPUs with double precision
29. Switching GPUs vclMatrix Double Precision tcrossprod (@test_switch_gpu_vclMatrix_algebra.R#859) - Less than 2 GPUs with double precision
30. Switching GPUs vclMatrix Double Precision transpose (@test_switch_gpu_vclMatrix_algebra.R#899) - Less than 2 GPUs with double precision
31. Switching GPUs vclMatrix integer class initializer (@test_switch_gpu_vclMatrix_classes.R#19) - Only one GPU available
32. Switching GPUs vclMatrix float class initializer (@test_switch_gpu_vclMatrix_classes.R#42) - Only one GPU available
33. Switching GPUs vclMatrix double class initializer (@test_switch_gpu_vclMatrix_classes.R#65) - Only one GPU available
34. Switching GPUs vclMatrix integer vector initializers (@test_switch_gpu_vclMatrix_classes.R#89) - Only one GPU available
35. Switching GPUs vclMatrix float vector initializers (@test_switch_gpu_vclMatrix_classes.R#111) - Only one GPU available
36. Switching GPUs vclMatrix double vector initializers (@test_switch_gpu_vclMatrix_classes.R#133) - Only one GPU available
37. Switching GPUs vclMatrix integer scalar initializers (@test_switch_gpu_vclMatrix_classes.R#156) - Only one GPU available
38. Switching GPUs vclMatrix float scalar initializers (@test_switch_gpu_vclMatrix_classes.R#181) - Only one GPU available
39. Switching GPUs vclMatrix double scalar initializers (@test_switch_gpu_vclMatrix_classes.R#205) - Only one GPU available
40. Switching GPUs vclMatrix Single Precision Pearson Covariance (@test_switch_gpu_vclMatrix_cov.R#24) - Only one GPU available
41. Switching GPUs vclMatrix Double Precision Pearson Covariance (@test_switch_gpu_vclMatrix_cov.R#46) - Only one GPU available
42. Switching GPU vclMatrix Single Precision Euclidean Distance (@test_switch_gpu_vclMatrix_dist.R#62) - Only one GPU available
43. Switching GPU vclMatrix Double Precision Euclidean Distance (@test_switch_gpu_vclMatrix_dist.R#86) - Only one GPU available
44. Switching GPU vclMatrix Single Precision Squared Euclidean Distance (@test_switch_gpu_vclMatrix_dist.R#109) - Only one GPU available
45. Switching GPU vclMatrix Double Precision Squared Euclidean Distance (@test_switch_gpu_vclMatrix_dist.R#131) - Only one GPU available
46. Switching GPU vclMatrix Single Precision Pairwise Euclidean Distance (@test_switch_gpu_vclMatrix_dist.R#154) - Only one GPU available
47. Switching GPU vclMatrix Double Precision Pairwise Euclidean Distance (@test_switch_gpu_vclMatrix_dist.R#190) - Only one GPU available
48. Switching GPU vclMatrix Single Precision Pairwise Squared Euclidean Distance (@test_switch_gpu_vclMatrix_dist.R#227) - Only one GPU available
49. Switching GPU vclMatrix Double Precision Pairwise Squared Euclidean Distance (@test_switch_gpu_vclMatrix_dist.R#263) - Only one GPU available
50. Switching GPU vclMatrix Symmetric Single Precision Matrix Eigen Decomposition (@test_switch_gpu_vclMatrix_eigen.R#30) - switching contexts not function in ViennaCL yet
51. Switching GPU vclMatrix Symmetric Double Precision Matrix Eigen Decomposition (@test_switch_gpu_vclMatrix_eigen.R#68) - switching contexts not function in ViennaCL yet
52. Switching GPU vclMatrix Single Precision Matrix Element-Wise Trignometry (@test_switch_gpu_vclMatrix_math.R#26) - Only one GPU available
53. Switching GPU vclMatrix Double Precision Matrix Element-Wise Trignometry (@test_switch_gpu_vclMatrix_math.R#97) - Only one GPU available
54. Switching GPU vclMatrix Single Precision Matrix Element-Wise Logs (@test_switch_gpu_vclMatrix_math.R#170) - Only one GPU available
55. Switching GPU vclMatrix Double Precision Matrix Element-Wise Logs (@test_switch_gpu_vclMatrix_math.R#207) - Only one GPU available
56. Switching GPU vclMatrix Single Precision Matrix Exponential (@test_switch_gpu_vclMatrix_math.R#246) - Only one GPU available
57. Switching GPU vclMatrix Double Precision Matrix Exponential (@test_switch_gpu_vclMatrix_math.R#269) - Only one GPU available
58. Switching GPU vclMatrix Single Precision Matrix Absolute Value (@test_switch_gpu_vclMatrix_math.R#294) - Only one GPU available
59. Switching GPU vclMatrix Double Precision Matrix Absolute Value (@test_switch_gpu_vclMatrix_math.R#317) - Only one GPU available
60. Switching GPU vclMatrix Single Precision Maximum/Minimum (@test_switch_gpu_vclMatrix_math.R#342) - Only one GPU available
61. Switching GPU vclMatrix Double Precision Maximum/Minimum (@test_switch_gpu_vclMatrix_math.R#367) - Only one GPU available
62. Switching GPU vclMatrix Single Precision Matrix sqrt (@test_switch_gpu_vclMatrix_math.R#394) - Only one GPU available
63. Switching GPU vclMatrix Double Precision Matrix sqrt (@test_switch_gpu_vclMatrix_math.R#417) - Only one GPU available
64. Switching GPU vclMatrix Single Precision Matrix sign (@test_switch_gpu_vclMatrix_math.R#441) - Only one GPU available
65. Switching GPU vclMatrix Double Precision Matrix sign (@test_switch_gpu_vclMatrix_math.R#464) - Only one GPU available
66. Switching GPU vclMatrix Single Precision Column Sums (@test_switch_gpu_vclMatrix_row_col.R#34) - Only one GPU available
67. Switching GPU vclMatrix Double Precision Column Sums (@test_switch_gpu_vclMatrix_row_col.R#56) - Only one GPU available
68. Switching GPU vclMatrix Single Precision Row Sums (@test_switch_gpu_vclMatrix_row_col.R#80) - Only one GPU available
69. Switching GPU vclMatrix Double Precision Row Sums (@test_switch_gpu_vclMatrix_row_col.R#102) - Only one GPU available
70. Switching GPU vclMatrix Single Precision Column Means (@test_switch_gpu_vclMatrix_row_col.R#125) - Only one GPU available
71. Switching GPU vclMatrix Double Precision Column Means (@test_switch_gpu_vclMatrix_row_col.R#147) - Only one GPU available
72. Switching GPU vclMatrix Single Precision Row Means (@test_switch_gpu_vclMatrix_row_col.R#171) - Only one GPU available
73. Switching GPU vclMatrix Double Precision Row Means (@test_switch_gpu_vclMatrix_row_col.R#193) - Only one GPU available
74. Switching GPU vclMatrix Single Precision cbind (@test_switch_gpu_vclMatrix_row_col.R#216) - Only one GPU available
75. Switching GPU vclMatrix Double Precision cbind (@test_switch_gpu_vclMatrix_row_col.R#254) - Only one GPU available
76. Switching GPU vclMatrix Single Precision rbind (@test_switch_gpu_vclMatrix_row_col.R#293) - Only one GPU available
77. Switching GPU vclMatrix Double Precision rbind (@test_switch_gpu_vclMatrix_row_col.R#331) - Only one GPU available
78. Switching GPU vclMatrix Single Precision Block Column Sums (@test_switch_gpu_vclMatrix_row_col.R#373) - Only one GPU available
79. Switching GPU vclMatrix Double Precision Block Column Sums (@test_switch_gpu_vclMatrix_row_col.R#396) - Only one GPU available
80. Switching GPU vclMatrix Single Precision Block Row Sums (@test_switch_gpu_vclMatrix_row_col.R#421) - Only one GPU available
81. Switching GPU vclMatrix Double Precision Block Row Sums (@test_switch_gpu_vclMatrix_row_col.R#444) - Only one GPU available
82. Switching GPU vclMatrix Single Precision Block Column Means (@test_switch_gpu_vclMatrix_row_col.R#468) - Only one GPU available
83. Switching GPU vclMatrix Double Precision Block Column Means (@test_switch_gpu_vclMatrix_row_col.R#490) - Only one GPU available
84. Switching GPU vclMatrix Single Precision Block Row Means (@test_switch_gpu_vclMatrix_row_col.R#514) - Only one GPU available
85. Switching GPU vclMatrix Double Precision Block Row Means (@test_switch_gpu_vclMatrix_row_col.R#536) - Only one GPU available
86. Switching GPU vclMatrix get element access (@test_switch_gpu_vclMatrix_utils.R#17) - Only one GPU available
87. Switching GPU vclMatrix set column access (@test_switch_gpu_vclMatrix_utils.R#62) - Only one GPU available
88. Switching GPU vclMatrix set row access (@test_switch_gpu_vclMatrix_utils.R#118) - Only one GPU available
89. Switching GPU vclMatrix set element access (@test_switch_gpu_vclMatrix_utils.R#173) - Only one GPU available
90. Switching GPU vclMatrixBlock Single Precision Block Matrix multiplication (@test_switch_gpu_vclMatrixBlock_algebra.R#26) - Only one GPU available
91. Switching GPU vclMatrixBlock Single Precision Matrix Subtraction (@test_switch_gpu_vclMatrixBlock_algebra.R#55) - Only one GPU available
92. Switching GPU vclMatrixBlock Single Precision Scalar Matrix Subtraction (@test_switch_gpu_vclMatrixBlock_algebra.R#86) - Only one GPU available
93. Switching GPU vclMatrixBlock Single Precision Unary Scalar Matrix Subtraction (@test_switch_gpu_vclMatrixBlock_algebra.R#119) - Only one GPU available
94. Switching GPU vclMatrixBlock Single Precision Matrix Addition (@test_switch_gpu_vclMatrixBlock_algebra.R#145) - Only one GPU available
95. Switching GPU vclMatrixBlock Single Precision Scalar Matrix Addition (@test_switch_gpu_vclMatrixBlock_algebra.R#176) - Only one GPU available
96. Switching GPU vclMatrixBlock Single Precision Matrix Element-Wise Multiplication (@test_switch_gpu_vclMatrixBlock_algebra.R#209) - Only one GPU available
97. Switching GPU vclMatrixBlock Single Precision Scalar Matrix Multiplication (@test_switch_gpu_vclMatrixBlock_algebra.R#239) - Only one GPU available
98. Switching GPU vclMatrixBlock Single Precision Matrix Element-Wise Division (@test_switch_gpu_vclMatrixBlock_algebra.R#271) - Only one GPU available
99. Switching GPU vclMatrixBlock Single Precision Scalar Matrix Division (@test_switch_gpu_vclMatrixBlock_algebra.R#302) - Only one GPU available
100. Switching GPU vclMatrixBlock Single Precision Matrix Element-Wise Power (@test_switch_gpu_vclMatrixBlock_algebra.R#335) - Only one GPU available
101. Switching GPU vclMatrixBlock Single Precision Scalar Matrix Power (@test_switch_gpu_vclMatrixBlock_algebra.R#366) - Only one GPU available
102. Switching GPU vclMatrixBlock Single Precision crossprod (@test_switch_gpu_vclMatrixBlock_algebra.R#392) - Only one GPU available
103. Switching GPU vclMatrixBlock Single Precision tcrossprod (@test_switch_gpu_vclMatrixBlock_algebra.R#435) - Only one GPU available
104. Switching GPU vclMatrixBlock Double Precision Block Matrix multiplication (@test_switch_gpu_vclMatrixBlock_algebra.R#480) - Only one GPU available
105. Switching GPU vclMatrixBlock Double Precision Matrix Subtraction (@test_switch_gpu_vclMatrixBlock_algebra.R#510) - Only one GPU available
106. Switching GPU vclMatrixBlock Double Precision Scalar Matrix Subtraction (@test_switch_gpu_vclMatrixBlock_algebra.R#542) - Only one GPU available
107. Switching GPU vclMatrixBlock Double Precision Unary Scalar Matrix Subtraction (@test_switch_gpu_vclMatrixBlock_algebra.R#577) - Only one GPU available
108. Switching GPU vclMatrixBlock Double Precision Matrix Addition (@test_switch_gpu_vclMatrixBlock_algebra.R#604) - Only one GPU available
109. Switching GPU vclMatrixBlock Double Precision Scalar Matrix Addition (@test_switch_gpu_vclMatrixBlock_algebra.R#636) - Only one GPU available
110. Switching GPU vclMatrixBlock Double Precision Matrix Element-Wise Multiplication (@test_switch_gpu_vclMatrixBlock_algebra.R#670) - Only one GPU available
111. Switching GPU vclMatrixBlock Double Precision Scalar Matrix Multiplication (@test_switch_gpu_vclMatrixBlock_algebra.R#701) - Only one GPU available
112. Switching GPU vclMatrixBlock Double Precision Matrix Element-Wise Division (@test_switch_gpu_vclMatrixBlock_algebra.R#735) - Only one GPU available
113. Switching GPU vclMatrixBlock Double Precision Scalar Matrix Division (@test_switch_gpu_vclMatrixBlock_algebra.R#767) - Only one GPU available
114. Switching GPU vclMatrixBlock Double Precision Matrix Element-Wise Power (@test_switch_gpu_vclMatrixBlock_algebra.R#801) - Only one GPU available
115. Switching GPU vclMatrixBlock Double Precision Scalar Matrix Power (@test_switch_gpu_vclMatrixBlock_algebra.R#833) - Only one GPU available
116. Switching GPU vclMatrixBlock Double Precision crossprod (@test_switch_gpu_vclMatrixBlock_algebra.R#860) - Only one GPU available
117. Switching GPU vclMatrixBlock Double Precision tcrossprod (@test_switch_gpu_vclMatrixBlock_algebra.R#904) - Only one GPU available
Failed -------------------------------------------------------------------------
1. Failure: gpuVector Single Precision Element-Wise Logs (@test_gpuVector_math.R#140)
fgpu_log2[, ] not equal to `R_log2`.
1/4 mismatches
[4] -3.83 - -3.83 == 4.5e-07
base log float matrix elements not equivalent
2. Error: Non-Shared memory between vclMatrix & vclVector (@test_shared_memory.R#53)
object 'type' not found
1: as.vclVector(gpuA) at /data/Installers/gpuR/tests/testthat/test_shared_memory.R:53
2: as.vclVector(gpuA) at /data/Installers/gpuR/R/generics.R:31
3: .local(object, type, ...)
4: new("fvclVector", address = vclMatTovclVec(object@address, shared, ctx_id, 6L), .context_index = object@.context_index,
.platform_index = object@.platform_index, .platform = object@.platform, .device_index = object@.device_index,
.device = object@.device) at /data/Installers/gpuR/R/methods-vclVector.R:40
5: initialize(value, ...)
6: initialize(value, ...)
7: vclMatTovclVec(object@address, shared, ctx_id, 6L) at /data/Installers/gpuR/R/methods-vclVector.R:40
3. Failure: vclVector Single Precision Matrix Element-Wise Logs (@test_vclVector_math.R#141)
fgpu_log2[, ] not equal to `R_log2`.
1/4 mismatches
[4] -3.83 - -3.83 == 4.5e-07
base log float matrix elements not equivalent
DONE ===========================================================================
>
>
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