Cuda python vs pycuda
Cuda python vs pycuda. In this video I introduc Python as programming language is increasingly gaining importance, especially in data science, scientific, and parallel programming. There are syntactical differences of course, but you should be able to perform What is the difference of performance between Cuda C/C++ and CuPy (python wrapper of CUDA)? if I need to do operations on array size 1 million which one will be good in terms of scalability and 如果上述步骤没有问题,可以得到结果:<Managed Device 0>。如果机器上没有GPU或没安装好上述包,会有报错。CUDA程序执行时会独霸一张卡,如果你的机器上有多张GPU卡,CUDA默认会选用0号卡。 Nov 29, 2016 · import pycuda. Jul 20, 2023 · CUDA安装:CUDA Toolkit Archive,选择适应CUDA版本的安装包下载 PyCUDA:Archived: Python Extension Packages for Windows ,页面搜索“pycuda”,下载合适pycuda版本号, pycuda‑2021. NVIDIA GPU Accelerated Computing on WSL 2 . If you want to do gpu programming using simple python syntax without using other frameworks like tensorflow, then take a look at this. Our goal is to help unify the Python CUDA ecosystem with a single standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python. Numba - An open source JIT compiler that translates a subset of Python and NumPy code into fast machine code. OS platform. PyCUDA provide abstractions like pycuda. cuda_GpuMat in Python) which serves as a primary data container. gpuarray. OpenCL is maintained by the Khronos Group, a not for profit industry consortium creating open standards for the authoring and acceleration of parallel computing, graphics, dynamic media, computer vision and sensor processing on a wide variety of platforms and devices, with CUDA vs CuPy: What are the differences? Introduction. Jun 7, 2022 · CUDA Python vs PyCUDA. That used to mean one thread per device. I have used pycuda a lot, but usually with mpi4py because I work mostly with clusters. Citing. We want to provide an ecosystem foundation to allow interoperability among different accelerated libraries. If you're not sure which to choose, learn more about installing packages. exe compiler file. The C code produces the correct results, but the Python code doesn't. 在所有可用的领域专用语言和 JIT 编译器中,Triton 或许与 Numba 最相似:内核被定义为修饰过的 Python 函数,并与实例网格上不同的 program_id 的同时启动。 Dr Brian Tuomanen has been working with CUDA and general-purpose GPU programming since 2014. Jun 7, 2022 · Both CUDA-Python and pyCUDA allow you to write GPU kernels using CUDA C++. There are syntactical differences of course, but you should be able to perform basic operations using either methodology. Jan 15, 2014 · I am trying to learn CUDA and using PyCUDA to write a simple matrix multiplication code. Installing Installation# Runtime Requirements#. Dec 30, 2019 · All you need to install yourself is the latest nvidia-driver (so that it works with the latest CUDA level and all older CUDA levels you use. Use this guide to install CUDA. OpenGL On systems which support OpenGL, NVIDIA's OpenGL implementation is provided with the CUDA Driver. compiler import SourceModule Note that you do not have to use pycuda. CUDA Python is a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python. If you don’t have a CUDA-capable GPU, you can access one of the thousands of GPUs available from cloud service providers, including Amazon AWS, Microsoft Azure, and IBM SoftLayer. Mar 10, 2023 · With PyCUDA, you can write CUDA programs in Python, which can be more convenient and easier to read than traditional CUDA C programs. Still I get this stra Jul 15, 2021 · Hello! For inference of trt-engines (they are obtained after onnx format using trtexec) I try to use PyCuda package. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. You need to get all your bananas lined up on the CUDA side of things first, then think about the best way to get this done in Python [shameless rep whoring, I know]. On the other hand, CuPy is a high-level Jun 21, 2022 · CUDA Python vs PyCUDA. 04? #Install CUDA on Ubuntu 20. PyCUDA is less popular than SWIG. But there will be a difference in the performance of the code you write in Python to setup or use the results of the pyCUDA kernel vs the one you write in C. Nov 17, 2021 · Using PyCUDA, however, you can rewrite specific functionality in CUDA that will benefit from the speed-up, while leaving everything else in Python. Ideally persistent. PyCUDA compiles CUDA C code and executes it. CuPy utilizes CUDA Toolkit libraries including cuBLAS, cuRAND, cuSOLVER, cuSPARSE, cuFFT, cuDNN and NCCL to make full use of the GPU architecture. _driver. I have installed the latest cuda drivers and I use a nvidia gpu with cuda support. strides is 12, 3, 1 (y, x, z), C-order and default according to the numpy. Several wrappers of the CUDA API already exist–so why the need for PyCUDA? Object cleanup tied to lifetime of objects. 1. driver as cuda from pycuda. 6. CUDA Python is supported on all platforms that CUDA is supported. In general, only pyCUDA is required when inferencing with TensorRT. I installed pycuda using the precompiled package: pycuda‑ See all the latest NVIDIA advances from GTC and other leading technology conferences—free. readthedocs. We can test it. 02 or later) Windows (456. system Closed June 21, 2022, 11:41pm 4. float32) a_gpu = cuda. def mul(d,f): g = torch. I am trying to install pycuda in computer with Windows 10 64bits, I installed the GPU Toolkit 9. GPU Arrays¶ Vector Types¶ class pycuda. He received his bachelor of science in electrical engineering from the University of Washington in Seattle, and briefly worked as a software engineer before switching to mathematics for graduate school. Python is actually quite common, and there are many frameworks for writing web servers in Python such as flask, bottle, django, etc. – Sep 1, 2012 · This is my pycuda code for rotation. 496093 Oct 24, 2017 · from pyfft. Nov 15, 2023 · PyCUDA是Python编程语言的扩展库,可以让开发者使用NVIDIA的CUDA平台编写GPU计算程序。它是一种CUDA的完全Python实现,使得开发者可以在Python环境中利用CUDA的并行计算能力。PyCUDA的主要特点包括: 编码更为灵活、迅速、自适应调节代码。 New in 0. Basically, my team is looking for a clean way to migrate test cases and development flows to be python-based, but still code kernels in C++ for inclusion into production environments that Nov 28, 2022 · CUDA is simply slower! To see this in the even more spectacular way i higly reccomend to install scikit-umfpack (using pip). Source Distributions Oct 9, 2020 · I am trying to install the PyCUDA module to run some python script I downloaded, but trying to install it with pip doesn't work. Accelerated Computing. For Cuda test program see cuda folder in the distribution. Mac OS 10. Download the file for your platform. (try numba instead of pyCUDA). OpenCL, the Open Computing Language, is the open standard for parallel programming of heterogeneous system. I got up in the morning and CUDA vs PyTorch: What are the differences? CUDA is a parallel computing platform and application programming interface model developed by NVIDIA, while PyTorch is an open-source machine learning framework primarily used for deep learning tasks. CUDA Python 12. Ease of Use: CUDA is a low-level parallel computing framework that requires programming in C or C++. Completeness. extern __shared__ float sdata[]; you are telling the compiler that the caller will provide the shared memory. 0 documentation Mar 8, 2012 · You don't need a context-per-kernel call, you need a context per GPU. All of CUDA’s supported vector types, such as float3 and long4 are available as numpy data types within this class. WSL or Windows Subsystem for Linux is a Windows feature that enables users to run native Linux applications, containers and command-line tools directly on Windows 11 and later OS builds. PyCUDA provides even more fine-grained control of the CUDA API. 3). GPUArray make CUDA programming even more convenient than with Nvidia's C-based runtime. The CUDA multi-GPU model is pretty straightforward pre 4. Architecturally, I wonder whether you really need the machine learning (which I imagine would be a data processing pipeline) and the web server to be the same process / binary In contrast, Numba relies on the CUDA memory management system and uses CUDA memory allocation functions for managing memory on the GPU. 2, PyCuda 2011. 1 and Anaconda 4. To keep data in GPU memory, OpenCV introduces a new class cv::gpu::GpuMat (or cv2. Numba supports compilation of Python to run on either CPU or GPU hardware and it's fundamentally written in Python. I've taken a few courses (3 years ago) with CUDA so I know it somewhat, but I spend 90% of my time in Python these days. 8. 04. PyCUDA knows about dependencies, too Abstractions like pycuda. 使用CUDA C++来对程序进行GPU加速无疑是最常用、高效且最灵活的方式,但是CUDA C++的学习成本与程序修改成本较高,对于大多数Python玩家而言不是很友好;PyCUDA是在Python中完全对接 CUDA C/C++ API,可以在 Python 中释放 NVIDIA GPU 性能的优先 Nov 18, 2013 · I am learning PyCUDA, and while going through the documentation on pycuda. 94. ndarray documentation, suggesting that z changes fastest. Numba CUDA Python 11. With it, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms, and supercomputers. I run this command for install PyCuda: pip3 install pycuda --user And I get a lot of erros… Some o fthem are below… Keyring is skipped due to an exception: Item does not exist! Collecting pycuda Downloading pycuda-2021. Heartful-echo June 7, 2022, 11:40pm 3. driver as cuda import pycuda. Aug 21, 2015 · It seems to be due to the strides/memory layout of the numpy. Further, it Jul 31, 2013 · Based on my reading of the PyCUDA documentation, the samples and the book on CUDA by Kirk and Hwu, I have successfully implemented a CUDA C-based complex matrix multiplication program and have also written a version in PyCUDA. 4版本的CUDA,python为3. 1: Support for CUDA gdb: $ cuda-gdb --args python -m pycuda. You can use one thread. However, usability often comes at the cost of performance and applications written in Python are considered to be much slower than applications written in C or FORTRAN. 0 - each GPU has its own context, and each context must be established by a different host thread. Python developers will be able to leverage massively parallel GPU computing to achieve faster results and accuracy. edu/me5013 Apr 10, 2019 · Hi all! Sorry if this is a common beginners question, but I’d love to get the community view on how to use pyCuda in a context where writing the kernel source (c++) code in a python string is not viable. Specific dependencies are as follows: Driver: Linux (450. Is there any suggestions? Jul 17, 2022 · At least for me, cuda/pycuda is better cuda than cuda/pyopencl. #How to Get Started with CUDA for Python on Ubuntu 20. Set Up CUDA Python. 86181641 -21146. The default superlu solver used in spsolve from scipy works using one core only, whereas umfpack boosts solution using all your CPUs. tools import make_default_context import pycuda. These numpy. 9版本 I used to find writing CUDA code rather terrifying. Toggle table of contents sidebar. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. I have also installed the cuda toolkit and pycuda drivers. usta. So good. Few CUDA Samples for Windows demonstrates CUDA-DirectX12 Interoperability, for building such samples one needs to install Windows 10 SDK or higher, with VS 2015 or VS 2017. In PyCUDA, that is done by specifying shared=nnnn on the line that calls the CUDA function. vec ¶. tar. autoinit as cudacontext random_tensor = torch. C++ code in CUDA makes more sense. Let's explore the key differences between them. PyCUDA also provides seamless integration with NumPy, See full list on developer. For two 4x4 randomly generated matrices I get the following solution: Cuda: [[ -5170. As @Wang has mentioned, Pycuda is faster than Numba. 0 Release notes# Released on October 3, 2022. 0 style multigpu with CUDA 4. 2. Mat) making the transition to the GPU module as smooth as possible. CUDA® Python provides Cython/Python wrappers for CUDA driver and runtime APIs; and is installable today by using PIP and Conda. Separately, both are working fine, but when I try to use pyCuda after Cupy, I got the following error: pycuda. autoinit – initialization, context creation, and cleanup can also be performed manually, if desired. compiler import SourceModule import numpy a = numpy. Jun 4, 2018 · For parallel processing in python some intermideate libraries or packages needed to be there that sit between the code and the gpu/cpu for parallel executions. autoinit from pycuda. Good morning. Its interface is similar to cv::Mat (cv2. mem_alloc(a. PyCUDA's documentation mentions Driver Interface calls in passing, but I'm a bit think and can't see how to get information such as 'SHARED_SIZE_BYTES' out of my code. Support for GPU Programming Models: While both CuPy and Numba support CUDA programming models, CuPy also provides support for OpenCL, which allows for greater flexibility in terms of hardware support. Some popular packages are pycuda, numba etc. lib files used to compile pycuda. 1+cuda114‑cp39‑cp39‑win_amd64. On devices where the L1 cache and shared memory use the same hardware resources, this sets through cacheConfig the preferred cache configuration for the current device. The problem is that running vcvars64. Categories: Foreign Function Interface. But then I discovered a couple of tricks that actually make it quite accessible. cuda import Plan import numpy import pycuda. GPUArray make CUDA programming even May 28, 2022 · One major issue most young data scientists, enthusiasts ask me is how to find the GPU IDs to map in the Pytorch code?. Full course available at: http://idl. cuda/pycuda is double performance. The key difference is that the host-side code in one case is coming from the community (Andreas K and others) whereas in the CUDA Python case it is coming from NVIDIA. import tensorrt as trt import torch import pycuda. cudaDeviceSetCacheConfig (cacheConfig: cudaFuncCache) # Sets the preferred cache configuration for the current device. Apr 26, 2022 · Now available on Stack Overflow for Teams! AI features where you work: search, IDE, and chat. 7 MB) | | 1. One limitation is memory transfer times. e. In the following tables “sp” stands for “single precision”, “dp” for “double precision”. 1 µs per loop CUDA vs Triton 编译器优化对比。 编程模型. nbytes) cuda. mul(d,f). However, it’s quite hard to do it properly, as PyCuda initializes its own CUDA contexts instead of using a default one, so sometimes you may end up in a situation where PyTorch pointers are inaccessible form PyCuda and vice versa. May 26, 2019 · Python version. cudart. I'm trying to figure out if it's even worth working with PyCuda or if I should just go straight into CUDA. CuPy is an open-source array library for GPU-accelerated computing with Python. to_gpu (also get) PyCUDA is more close to CUDA C. * Some content may require login to our free NVIDIA Developer Program. . If you look inside their numba_vm, you will find they used just @jit change it if you like @njit (parallel=True) Apr 30, 2024 · PyCudaは、NVIDIAが提供するCUDAパラレルコンピューティングプラットフォームをPythonから利用するためのオープンソースライブラリです。CUDAを使用することで、GPUの強力な並列計算能力を活用し、CPUよりも高速に処理を実行できます。PyCudaを使えば、Pythonの親しみやすい文法でGPUプログラミングを PyOpenCL¶. More recently, Nvidia released the official CUDA Python, which will surely enrich the ecosystem $ sudo apt-get install build-essential python-dev python-setuptools libboost-python-dev libboost-thread-dev -y Download pyCUDA and unpack it $ tar xzvf pycuda-VERSION. However, A. ndarray. fftn. I know there is a library called pyculib, but I always failed to install it using conda install pyculib. io Installation OK, so I fixed it for me. Version or git revision of scikit-cuda. nvidia. May 21, 2024 · CUDA Python Low-level Bindings. There are two basic approaches supported by Numba: ufuncs/gufuncs (subject of the rest of this notebook) CUDA Python kernels (subject of next notebook) Making new ufuncs for the GPU Toggle Light / Dark / Auto color theme. Jan 2, 2024 · import pycuda. Hightlights# Support CUDA Toolkit 11. I would rather implement as C++ CUDA library and create cython interfaces. この記事についてJetson NanoにGPU(CUDA)が有効なOpenCVをインストールPythonでOpenCVのCUDA関数を使って、画像処理(リサイズ)を行うC++でOpenCVのC… Jul 7, 2020 · Is it something to do with cuda contexts clashing between pycuda and pytorch? I can include more code if necessary. Aug 29, 2024 · CUDA on WSL User Guide. py Automatically: Sets Compiler ags Retains source code Disables compiler cache Feb 16, 2018 · I have installed pycuda and scikit cuda via pip install on python 3. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code El propósito principal de las notas es mostrar lo básico de CUDA y PyCUDA para que al completar la lectura y los ejercicios, el lector sea capaz de hacer sus propios programas en paralelo y tenga la posibilidad de acercarse a libros de enseñanza de CUDA tales como CUDA by Example o Programming Massively Parallel Processors y el lenguaje no May 22, 2017 · Both C++ and Python are perfectly reasonable languages for implementing web servers. CUDA and PyCUDA version. What you want, is to change the path yourself: add the path to the cl. 22 times longer than the fastest. So the idea in Jan 2, 2024 · PyCUDA lets you access Nvidia’s CUDA parallel computation API from Python. PyCUDA puts the full power of CUDA's driver API at your disposal, if you wish. I have not tried CUDA 4. gz Apr 19, 2013 · As someone working in scientific computing using both Python and CUDA (via PyCUDA): The attractiveness of Python (especially with numpy) is the ability to shorten my development time, and the attractiveness of CUDA is to shorten my run time. 1, nVidia GeForce 9600M, 32 Mb buffer: Feb 19, 2017 · It is possible and you can find an example here. device("cuda" if torch. Nov 19, 2017 · Coding directly in Python functions that will be executed on GPU may allow to remove bottlenecks while keeping the code short and simple. Several wrappers of the CUDA API already exist-so what’s so special about PyCUDA? Object cleanup tied to lifetime of objects. 2 Numba与CUDA C++、PyCUDA的对比. The kernel is presented as a string to the python code to compile and run. cuda() # I explicitly called cuda() which is not necessary return g When call the function above as %timeit mul(x,y) Returns: The slowest run took 10. Your best choice is to use numpy ndarrays. The NVIDIA® CUDA® Toolkit provides a development environment for creating high-performance, GPU-accelerated applications. Jan 4, 2024 · pyvkfft offers a simple python interface to the CUDA and OpenCL backends of VkFFT, compatible with pyCUDA, CuPy and pyOpenCL. Numba’s CUDA JIT (available via decorator or function call) compiles CUDA Python functions at run time, specializing them Sep 30, 2021 · The most convenient way to do so for a Python application is to use a PyCUDA extension that allows you to write CUDA C/C++ code in Python strings. Pyfft tests were executed with fast_math=True (default option for performance test script). 10000 loops, best of 3: 50. random. cuda. You could possibly make it work if you use some kind of message queue like Celery, where the HTTP request places a job on the queue and the worker on the other side of the queue runs the CUDA program. 5 64bits. #we write the Jan 25, 2023 · So I try python -m pip install pycuda and it fails (Here's some of the output from the failed install): \Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12. Learn more Explore Teams Jul 26, 2018 · In python, what is the best to run fft using cuda gpu computation? I am using pyfftw to accelerate the fftn, which is about 5x faster than numpy. 1. Jul 18, 2017 · It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. I want to use pycuda to accelerate the fft. 80. I run pip install pycuda on the command line At first, I get this: Sep 19, 2013 · Numba exposes the CUDA programming model, just like in CUDA C/C++, but using pure python syntax, so that programmers can create custom, tuned parallel kernels without leaving the comforts and advantages of Python behind. If you want to start at PyCUDA, their documentation is good to start. . 6, Cuda 3. Edit: A quick and easy way would be to use Python Subprocess check_output function CUDA - It provides everything you need to develop GPU-accelerated applications. memcpy_htod(a_gpu,a)#transfer the data to the GPU #executing a kernel #function: write code to double each entry in a_gpu. SourceModule and pycuda. Jul 22, 2021 · Hi all, I’m trying to do some operations on pyCuda and Cupy. it's easy to install and implement. device = torch. Please find this sample for more information: /usr/src/tensorrt/samples/python/end_to_end_tensorflow_mnist. 6, Python 2. This topic was automatically closed 14 Jan 2, 2024 · Welcome to PyCUDA’s documentation!¶ PyCUDA gives you easy, Pythonic access to Nvidia’s CUDA parallel computation API. May 30, 2018 · PyCUDA may not be compatible with WSGI web server contexts. 38 or later) I've written up the kernel in PyCuda but I'm running into some issues and there's just not great documentation is seems. In this post, we will explore the key differences between CUDA and CuPy, two popular frameworks for accelerating scientific computations on GPUs. something like This is a very short introduction with simple examples for the CUDA Python interface PyCUDA. 0, it doesn't. The documentation can be found at https://pyvkfft. Jun 3, 2015 · PyCUDA requires that objects passed through to the CUDA APIs support the Python buffer protocol. dtype instances have field names of x, y, z, and w just like their CUDA counter May 14, 2019 · PyCUDA lets you access Nvidia’s CUDA parallel computation API from Python. Aug 1, 2024 · Download files. randin 这些代码原是为樊哲勇老师的书籍<<CUDA-Programming编程>>编写的示例代码。为了让CUDA初学者在python中更好的使用CUDA Apr 22, 2016 · In case someone is still looking for an answer: configure. py generates a siteconf. gpuarray, I am puzzled by the difference between pycuda. First off you need to download CUDA drivers and install it on a machine with a CUDA-capable GPU. Sep 4, 2022 · CuPy offers both high level functions which rely on CUDA under the hood, low-level CUDA support for integrating kernels written in C, and JIT-able Python functions (similar to Numba). Oct 28, 2011 · With pyCUDA you will be writing the CUDA kernels using C++, and it's CUDA, so there shouldn't be a difference in performance of running that code. randn(4,4) a = a. Thanks. Numba is a compiler so this is not related to the CUDA usage. 2 with python 3. Jun 7, 2022 · The key difference is that the host-side code in one case is coming from the community (Andreas K and others) whereas in the CUDA Python case it is coming from NVIDIA. PyCUDA requires same effort as learning CUDA C. autoi Feb 13, 2019 · I have lots of cuda kernels to test so I would like to be able to test them by executing them from a python program (the python program calls a library that launches cuda kernels) i. ) This has many advantages over the pip install tensorflow-gpu method: Anaconda will always install the CUDA and CuDNN version that the TensorFlow code was compiled to use. memcpy_htod (also _dtoh) and pycuda. If you use scikit-cuda in a scholarly publication, please cite Oct 12, 2018 · 初心者向けにPythonでCUDAを利用する方法について現役エンジニアが解説しています。CUDAとはNVIDIA社が開発・提供しているGPU向けの並立コンピューティングプラットフォームです。CUDAを使う前提条件や必要なソフトのインストール方法、PyCUDAのインストール方法などについて解説します。 Jun 8, 2015 · When you specify. 7 MB 530 kB/s Installing build Compare PyCUDA and SWIG's popularity and activity. astype(numpy. gz (1. com Oct 7, 2020 · So it’s recommended to use pyCUDA to explore CUDA with python. 6 and have been trying to run the following example from scikit cuda: from __future__ import print_function import pycuda. CUDA Programming and Performance. signal import butter Sep 15, 2020 · Basic Block – GpuMat. is_available() else "cpu") Mar 11, 2021 · The first post in this series was a python pandas tutorial where we introduced RAPIDS cuDF, the RAPIDS CUDA DataFrame library for processing large amounts of data on an NVIDIA GPU. Checkout the Overview for the workflow and performance results. gpuarray as gpuarray from Numba’s compiler pipeline for transforming Python functions to machine code can be used to generate CUDA functions which can be used standalone or with CuPy. driver. The code assumes that x changes fastest, then y, and z slowest. Source builds allow for missing types and APIs. ones(1) sample_tensor = torch. The documentation mentions this explicitly. The guide for using NVIDIA CUDA on Windows Subsystem for Linux. Can anyone point me to any To install this package run one of the following: conda install conda-forge::cuda-python Description CUDA Python provides a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python. cuda. whl 表示11. This could mean hat an intermediate result is being cached. bat sets the path environment in a subshell and then closes it, so the set path disappears again. py file containing the paths to CUDA . LogicError: cuFuncSetBlockShape failed: invalid resource handle Do you know how I could fix it? Here is a simplified code to reproduce the error: import numpy as np import cupy as cp from scipy. In this introduction, we show one way to use CUDA in Python, and explain some basic principles of CUDA programming. CUDA. High performance with GPU. debug demo. As of CUDA 4. To make it cuda/pycuda, all what is needed it change their backend in just two files kernel_vm and benchmark_vm. In this tutorial, we discuss how cuDF is almost an in-place replacement for pandas. It is faster and easier to learn than classical programming languages such as C. Contribute to NVIDIA/cuda-python development by creating an account on GitHub. To run CUDA Python, you’ll need the CUDA Toolkit installed on a system with CUDA-capable GPUs. driver as cudadriver import pycuda. xsrrusjb ycwxk qbhby agvaz zyuygo apf bhyuiv ajdvc ifrinq iitn