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[BUG] Binary release with CUDA failing in Google Colab environment #3142
Description
I am setting up google colab playground to run some experiments.
Here is the notebook with helloworld example which fails when running with GPU kernel, while works fine on CPU:
https://colab.research.google.com/drive/1xUVsyKJgD7pvPDRnhliYzzqjcctpK7E7?usp=sharing
ArrayFire Exception (Internal error:998): In function void print(const char*, af_array, int, std::ostream&, bool) [with T = float; af_array = void*; std::ostream = std::basic_ostream<char>] In file src/api/c/print.cpp:99
Description
-
Did you build ArrayFire yourself or did you use the official installers
Using binary installer: https://arrayfire.s3.amazonaws.com/3.8.0/ArrayFire-v3.8.0_Linux_x86_64.sh -
Which backend is experiencing this issue? (CPU, CUDA, OpenCL)
CUDA throws exception
CPU works as expected -
Do you have a workaround?
No -
Can the bug be reproduced reliably on your system?
Yes -
Run your executable with AF_TRACE=all and AF_PRINT_ERRORS=1 environment
variables set.
The relevant error is likely
Unable to open /root/.arrayfire/KER11230367656483596278_CU_60_AF_38.bin for Tesla P100-PCIE-16GB
You can see full trace in the notebook. -
Screenshot or terminal output of the results
N/A, check colab link.
Reproducible Code and/or Steps
Easily reproducible, try running my shared colab notebook.
To ensure you are running on GPU, select Change Runtime -> Change runtime type -> GPU.
System Information
ArrayFire v3.8.0 (CUDA, 64-bit Linux, build d99887a) Platform: CUDA Runtime 11.0, Driver: 460.32.03 [0] Tesla P100-PCIE-16GB, 16281 MB, CUDA Compute 6.0
Run one of the following commands based on your OS
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 18.04.5 LTS
Release: 18.04
Codename: bionic
Failed to initialize NVML: Driver/library version mismatch
rocm-smi not found.
Number of platforms 1
Platform Name NVIDIA CUDA
Platform Vendor NVIDIA Corporation
Platform Version OpenCL 3.0 CUDA 11.3.101
Platform Profile FULL_PROFILE
Platform Extensions cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_icd cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_copy_opts cl_nv_create_buffer cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_nv_kernel_attribute cl_khr_device_uuid
Platform Host timer resolution 0ns
Platform Extensions function suffix NV
Platform Name NVIDIA CUDA
Number of devices 1
Device Name Tesla P100-PCIE-16GB
Device Vendor NVIDIA Corporation
Device Vendor ID 0x10de
Device Version OpenCL 3.0 CUDA
Driver Version 465.27
Device OpenCL C Version OpenCL C 1.2
Device Type GPU
Device Topology (NV) PCI-E, 00:00.4
Device Profile FULL_PROFILE
Device Available Yes
Compiler Available Yes
Linker Available Yes
Max compute units 56
Max clock frequency 1328MHz
Compute Capability (NV) 6.0
Device Partition (core)
Max number of sub-devices 1
Supported partition types None
Max work item dimensions 3
Max work item sizes 1024x1024x64
Max work group size 1024
Preferred work group size multiple 32
Warp size (NV) 32
Max sub-groups per work group 0
Preferred / native vector sizes
char 1 / 1
short 1 / 1
int 1 / 1
long 1 / 1
half 0 / 0 (n/a)
float 1 / 1
double 1 / 1 (cl_khr_fp64)
Half-precision Floating-point support (n/a)
Single-precision Floating-point support (core)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Correctly-rounded divide and sqrt operations Yes
Double-precision Floating-point support (cl_khr_fp64)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Address bits 64, Little-Endian
Global memory size 17071734784 (15.9GiB)
Error Correction support Yes
Max memory allocation 4267933696 (3.975GiB)
Unified memory for Host and Device No
Integrated memory (NV) No
Shared Virtual Memory (SVM) capabilities (core)
Coarse-grained buffer sharing Yes
Fine-grained buffer sharing No
Fine-grained system sharing No
Atomics No
Minimum alignment for any data type 128 bytes
Alignment of base address 4096 bits (512 bytes)
Preferred alignment for atomics
SVM 0 bytes
Global 0 bytes
Local 0 bytes
Max size for global variable 0
Preferred total size of global vars 0
Global Memory cache type Read/Write
Global Memory cache size 1376256 (1.312MiB)
Global Memory cache line size 128 bytes
Image support Yes
Max number of samplers per kernel 32
Max size for 1D images from buffer 268435456 pixels
Max 1D or 2D image array size 2048 images
Max 2D image size 16384x32768 pixels
Max 3D image size 16384x16384x16384 pixels
Max number of read image args 256
Max number of write image args 16
Max number of read/write image args 0
Max number of pipe args 0
Max active pipe reservations 0
Max pipe packet size 0
Local memory type Local
Local memory size 49152 (48KiB)
Registers per block (NV) 65536
Max number of constant args 9
Max constant buffer size 65536 (64KiB)
Max size of kernel argument 4352 (4.25KiB)
Queue properties (on host)
Out-of-order execution Yes
Profiling Yes
Queue properties (on device)
Out-of-order execution No
Profiling No
Preferred size 0
Max size 0
Max queues on device 0
Max events on device 0
Prefer user sync for interop No
Profiling timer resolution 1000ns
Execution capabilities
Run OpenCL kernels Yes
Run native kernels No
Sub-group independent forward progress No
Kernel execution timeout (NV) No
Concurrent copy and kernel execution (NV) Yes
Number of async copy engines 2
IL version
printf() buffer size 1048576 (1024KiB)
Activity
umar456 commented on Jun 8, 2021
Thanks for the link to your notebook. It really helps with the debugging of this issue.
Looks like the nvidia runtime is looking for a different nvrtc-buildtin library than provided by the installer. This is strange because we have tested this sceanario in our CI and it has been working correctly. I will need to investigate why this is happening. in the meantime you can work around this bug by renaming the
/opt/arrayfire/lib64/libnvrtc-builtins.so to /opt/arrayfire/lib64/libnvrtc-builtins.so.11.1
webshared commented on Jun 8, 2021
@umar456 thanks for checking this out! It looks like CUDA 11.* is not supported in the binary installer https://arrayfire.s3.amazonaws.com/3.8.0/ArrayFire-v3.8.0_Linux_x86_64.sh
You can see the error in my colab notebook:
Today I tried building from source in another colab and it worked, though it took hours to build everything so this is not practical to use in colab.
As I see you have different CUDA 10 and 11 distributions, maybe you can just build one with CUDA 11 support for Linux?
http://arrayfire.s3.amazonaws.com/index.html#!/3.8.0%2F
Having nightly builds would also be awesome: http://arrayfire.s3.amazonaws.com/index.html#!/nightly%2F
umar456 commented on Jun 8, 2021
That message is not an error. It is targeted towards ArrayFire developers to update the minimum driver requirements for the latest CUDA toolkits. CUDA maintains a forward compatibility and therefore older versions of the toolkit would work and do not need intervention. We use the toolkit versions to display useful error message if the CUDA driver doesn't support a certain version of the CUDA toolkit. Since we do not know the minimum driver versions of the future versions of CUDA we allow the program to execute without those checks if the Toolkit version is not found in the database. In those cases we add the message to only the AF_TRACE logs. If you remove the AF_TRACE environment variable, you will not see that message. The toolkit version has also been updated in the latest master and should be part of the next release(see #3125).
webshared commented on Jun 8, 2021
It makes sense, thanks! Adding this line fixed the issue in my colab:
9prady9 commented on Jun 21, 2021
Closing this as duplicate of issue fixed via #3105. @umar456 @atolkachiov if you feel some case is missing in the fix, feel free to reopen and we can fix accordingly.