oneMKL | oneAPI Math Kernel Library Interfaces | GPU library

by oneapi-src C++ Version: v0.2 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | oneMKL Summary

oneMKL is a C++ library typically used in Hardware, GPU applications. oneMKL has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

oneMKL interfaces are an open-source implementation of the oneMKL Data Parallel C++ (DPC++) interface according to the oneMKL specification. It works with multiple devices (backends) using device-specific libraries underneath. oneMKL is part of oneAPI.

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Support

oneMKL has a low active ecosystem.

It has 430 star(s) with 112 fork(s). There are 44 watchers for this library.

It had no major release in the last 12 months.

There are 26 open issues and 74 have been closed. On average issues are closed in 74 days. There are 18 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of oneMKL is v0.2

Quality

oneMKL has 0 bugs and 0 code smells.

Security

oneMKL has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

oneMKL code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

oneMKL is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

oneMKL releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 956 lines of code, 27 functions and 10 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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oneMKL Key Features

No Key Features are available at this moment for oneMKL.

oneMKL Examples and Code Snippets

No Code Snippets are available at this moment for oneMKL.

Community Discussions

Trending Discussions on oneMKL

Intel MKL ERROR: incorrect parameter when calling gemm()

Using Intel oneAPI MKL to perform sparse matrix with dense vector multiplication

Problem using MKL Trust Region EXAMPLE_EX_NLSQP_F90_X in Fortran

gfortran links with MKL leads to 'Intel MKL ERROR: Parameter 10 was incorrect on entry to DGEMM'

Unable to load shared library when running on .NET interactive on Mac

QUESTION

Intel MKL ERROR: incorrect parameter when calling gemm()

Asked 2022-Mar-21 at 08:09

I have this code:

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ANSWER

Answered 2022-Mar-21 at 06:47

By default most of the compilers take integers ( 'int' for C or C++ / 'INTEGER' for Fortran) as 32-bit length. So most applications need to be linked with LP64 MKL libraries. (https://www.intel.com/content/www/us/en/develop/documentation/onemkl-linux-developer-guide/top/linking-your-application-with-onemkl/linking-in-detail/linking-with-interface-libraries/using-the-ilp64-interface-vs-lp64-interface.html)

So try linking against LP64 interface and see if it works. Additionally, I would suggest you set MKL_VERBOSE=1 (https://www.intel.com/content/www/us/en/develop/documentation/onemkl-linux-developer-guide/top/managing-output/using-onemkl-verbose-mode.html) and then run your code so that you can see what parameters are passed to the function (as your error message says so).

You can also refer to the examples which comes with oneMKL.There is a similar example under the mkl directory location in your system as below \oneAPI\mkl\2022.0.2\examples\examples_dpcpp\dpcpp\blas\source with usm_gemm.cpp file name which I presume should help you.

Source https://stackoverflow.com/questions/71471488

QUESTION

Using Intel oneAPI MKL to perform sparse matrix with dense vector multiplication

Asked 2022-Mar-14 at 11:03

I am developing a program, making heavy use of Armadillo library. I have the 10.8.2 version, linked against Intel oneAPI MKL 2022.0.2. At some point, I need to perform many sparse matrix times dense vector multiplications, both of which are defined using Armadillo structures. I have found this point to be a probable bottleneck, and was being curious if replacing the Armadillo multiplication with "bare bones" sparse CBLAS routines from MKL (mkl_sparse_d_mv) would speed things up. But in order to do so, I need to convert from Armadillo's SpMat to something that MKL understands. As per Armadillo docs, sparse matrices are stored in CSC format, so I have tried mkl_sparse_d_create_csc. My attempt at this is below:

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ANSWER

Answered 2022-Mar-14 at 11:03

Yes, the cols_end array is incorrect as pointed out by CJR. They should be indexed as 2,3,4,5. Please see the documentation regarding the parameter to the function mkl_sparse_d_create_csc

cols_end:

This array contains col indices, such that cols_end[i] - ind - 1 is the last index of col i in the arrays values and row_indx. ind takes 0 for zero-based indexing and 1 for one-based indexing.

https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-c/top/blas-and-sparse-blas-routines/inspector-executor-sparse-blas-routines/matrix-manipulation-routines/mkl-sparse-create-csc.html

Change this line

cols_end[i] = static_cast((--X.end_col(i)).pos());

cols_end[i] = static_cast((X.end_col(i)).pos());

Now recompile and run the code. I've tested it and it is showing the correct results. Image with results and compilation command

Source https://stackoverflow.com/questions/71456912

QUESTION

Problem using MKL Trust Region EXAMPLE_EX_NLSQP_F90_X in Fortran

Asked 2022-Mar-10 at 17:51

I am using the example provided EXAMPLE_EX_NLSQP_F90_X.f90 in the folder of Intel oneAPI Math Kernel Library directory. This compile successfully, but when I run it in debug mode, the result of dtrnlsp_init in line 108 is TR_INVALID_OPTION.

Can you please offer me some suggestions on how to run it correctly? Thank you in advance.

The Fortran code of the example is:

...

ANSWER

Answered 2022-Mar-10 at 17:51

I already received a solution on the Intel forum. The problem was that I was using 4-byte integers in the program variables and linking mkl_intel_ilp64.lib when the proper library should be mkl_intel_lp64.lib. You can see the solution here: Solution

Source https://stackoverflow.com/questions/71416760

QUESTION

gfortran links with MKL leads to 'Intel MKL ERROR: Parameter 10 was incorrect on entry to DGEMM'

Asked 2021-Dec-21 at 11:55

I have the following Fortran code (modified on top of many answers from stack overflow..)

...

ANSWER

Answered 2021-Dec-21 at 11:55

You selected the ilp64 version of MKL. That means that integers, longs and pointers are 64-bit. But you are not using gfortran with 64-bit integers, the default in all compilers I know is 32-bit integers. Either you want a different version of MKL, like lp64, or you want to set up your gfortran to use 64-bit default integers. For the former, select the 32bit-integer interface layer in the Link Advisor.

Source https://stackoverflow.com/questions/70429724

QUESTION

Unable to load shared library when running on .NET interactive on Mac

Asked 2021-Jun-26 at 11:38

I am trying to run a code that uses MKL native library in a .ipynb .NET notebook and I get the following error.

Error: System.DllNotFoundException: Unable to load shared library 'libmkl_rt.dylib' or one of its dependencies. In order to help diagnose loading problems, consider setting the DYLD_PRINT_LIBRARIES environment variable: dlopen(liblibmkl_rt.dylib, 1): image not found

The code runs as expected if I run it with dotnet run but it doesn't on the interactive notebook. What could be the solution? (ps using .NET 5.0)

...

ANSWER

Answered 2021-Jun-26 at 11:38

Copying the native libraries into /usr/local/lib solved the problem.

Source https://stackoverflow.com/questions/68132215

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install oneMKL

Install Intel(R) oneAPI DPC++ Compiler (select variant as per requirement). Clone this project to <path to onemkl>, where <path to onemkl> is the root directory of this repository. You can Build with Conan to automate the process of getting dependencies or you can download and install the required dependencies manually and Build with CMake directly.
Install Intel(R) oneAPI DPC++ Compiler (select variant as per requirement).
Clone this project to <path to onemkl>, where <path to onemkl> is the root directory of this repository.
You can Build with Conan to automate the process of getting dependencies or you can download and install the required dependencies manually and Build with CMake directly.
Conan stores all files and data in ~/.conan. If you are fine with this behavior, you can skip to Conan Profiles section. To change this behavior, set the environment variable CONAN_USER_HOME to a path of your choice. A .conan/ directory will be created in this path and future Conan commands will use this directory to find configuration files and download dependent packages. Packages will be downloaded into $CONAN_USER_HOME/data. To change the "/data" part of this directory, refer to the [storage] section of conan.conf file. To make this setting persistent across terminal sessions, you can add below line to your ~/.bashrc or custom runscript. Refer to Conan Documentation for more details.
Intel(R) oneAPI DPC++ Compiler for x86 CPU and Intel GPU backend: inteldpcpp_lnx
Open the profile you wish to use from <path to onemkl>/conan/profiles/ and set COMPILER_PREFIX to the path to the root folder of compiler. The root folder is the one that contains the bin and lib directories. For example, Intel(R) oneAPI DPC++ Compiler root folder for default installation on Linux is /opt/intel/inteloneapi/compiler/<version>/linux. User can define custom path for installing the compiler.
You can customize the [env] section of the profile based on individual requirements.
Install configurations for this project:
The following options are available to pass on conan install when building the oneMKL library:.
build_shared_libs=[True | False]. Setting it to True enables the building of dynamic libraries. The default value is True.
target_domains=[<list of values>]. Setting it to blas or any other list of domain(s), enables building of those specific domain(s) only. If not defined, the default value is all supported domains.
enable_mklcpu_backend=[True | False]. Setting it to True enables the building of oneMKL mklcpu backend. The default value is True.
enable_mklgpu_backend=[True | False]. Setting it to True enables the building of oneMKL mklgpu backend. The default value is True.
enable_mklcpu_thread_tbb=[True | False]. Setting it to True enables oneMKL on CPU with TBB threading instead of sequential. The default value is True.
build_functional_tests=[True | False]. Setting it to True enables the building of functional tests. The default value is True.
build_doc=[True | False]. Setting it to True enables the building of rst files to generate HTML files for updated documentation. The default value is False.
All options specified in the Conan section are available to CMake. You can specify these options using -D<cmake_option>=<value>. The following table provides a detailed mapping of options between Conan and CMake.