How to write cuda code

How to write cuda code. The only way to seriously micro-optimize your code (assuming you have already chosen the best possible algorithm) is to have a deep understanding of the GPU architecture, particularly with regard to using shared memory, external memory access patterns, register usage, thread occupancy, warps, etc. Dec 13, 2008 · Rumor has it that nVidia’s next release of CUDA will allow the compiler to convert CUDA code to standard multithreaded code so that it runs seamlessly on any computer with or without an nVidia GPU (though obviously, you’ll only get the real speedup if it does). But then I discovered a couple of tricks that actually make it quite accessible. Portability and readability. compiler. Basic approaches to GPU Computing. As usual, we will learn how to deal with those subjects in CUDA by coding. Manage code changes Feb 24, 2012 · My answer to this recent question likely describes what you need. i. zeros(4,3) a = a. Threads Sep 25, 2017 · Learn how to write, compile, and run a simple C program on your GPU using Microsoft Visual Studio with the Nsight plug-in. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance. As of now, you can install the toolkit and SDK and start writing programs. 4. To do it properly, I need to modify the kernel. Nov 18, 2017 · How to write the CUDA code to print out the CUDA capable devices on the computer. RAPIDS cuDF, being a GPU library built on top of NVIDIA CUDA, cannot take regular Python code and simply run it on a GPU. If I run the code with only this change, it will do the computation once per thread, rather than spreading the computation across the parallel threads. (For that, rather than writing my own kernel PTX code, I will use the one from the vectorAddDrv sample code, from the CUDA 11. 1. There are multiple ways to Nov 24, 2023 · AITemplate is a Python framework which renders neural network into high performance CUDA/HIP C++ code. As an alternative to using nvcc to compile CUDA C++ device code, NVRTC can be used to compile CUDA C++ device code to PTX at runtime. #include <stdio. 2019/01/02: I wrote another up-to-date tutorial on how to make a pytorch C++/CUDA extension with a Makefile. Important Note: To check the following code is working or not, write that code in a separate code block and Run that only again when you update the code and re running it. /inner_product_with_testbench. The code is based on the pytorch C extension example. There are many CUDA code samples included as part of the CUDA Toolkit to help you get started on the path of writing software with CUDA C/C++. com/pure-virtual-cpp-event-2021/Julia gives a peek into the state and future of CUDA Aug 29, 2024 · (Note that the CUDA compiler considers any device code that does not contribute to a write to global memory as dead code subject to elimination, so we must at least write something out to global memory as a result of our addressing logic in order to successfully apply this strategy. Shared Memory Example. Optimizing the computations for locality and parallelism is very time-consuming and error-prone and it often requires experts who have spent a lot of time learning how to write CUDA code. The oneAPI for NVIDIA GPUs from Codeplay allowed me to create binaries for NVIDIA or Intel GPUs easily. This way you can very closely approximate CUDA C/C++ using only Python without the need to allocate memory yourself. this code). If done correctly, "Hello, CUDA!" should be output Jan 9, 2021 · I am trying to run two cuda streams in parallel, I initiate the streams then use them to run computations in the processes. The profiler allows the same level of investigation as with CUDA C++ code. In CUDA, the code you write will be executed by multiple threads at once (often hundreds or thousands). The CUDA code used as an example isn't that important, but it would be nice to see something complete, that works. The rest of this note will walk through a practical example of writing and using a C++ (and CUDA) extension. #CUDA as C/C++ Extension cpu Cuda:{number ID of GPU} When initializing a tensor, it is often put directly on a CPU. We can do the same for CUDA. The goal of this application is very simple. You don’t need graphics experience. This post dives into CUDA C++ with a simple, step-by-step parallel programming example. cu): vectorAdd_kernel. To write Python code that uses CUDA, you can use the `torch` library. cpp files compiled with g++. A CUDA kernel is a small piece of code that performs a computation on each element of an input list. 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. Oct 31, 2012 · CUDA C is essentially C/C++ with a few extensions that allow one to execute functions on the GPU using many threads in parallel. Apr 17, 2024 · The code above adds two vectors A and B, of size N and stores the result into vector C. device(dev) a = torch. Using the CUDA Toolkit you can accelerate your C or C++ applications by updating the computationally intensive portions of your code to run on GPUs. To accelerate your applications, you can call functions from drop-in libraries as well as develop custom applications using languages including C, C++, Fortran and Python. Jul 28, 2021 · We’re releasing Triton 1. PyTorch supports the construction of CUDA graphs using stream capture, which puts a CUDA stream in capture mode. The data structures, APIs, and code described in this section are subject to change in future CUDA releases. C# code is linked to the PTX in the CUDA source view, as Figure 3 shows. In this tutorial, we will look at a simple vector addition program, which is often used as the "Hello, World!" of GPU computing. This tutorial will cover the basics of how to write a kernel, and how to organize threads, blocks, and grids. (Those familiar with CUDA C or another interface to CUDA can jump to the next section). cuda; nvidia; gpgpu; Share. NVRTC is a runtime compilation library for CUDA C++; more information can be found in the NVRTC User guide. Figure 3. The code is compiled using the NVIDIA CUDA Compiler (nvcc) and executed on the GPU. Here is an example of a simple CUDA program that adds two arrays: import numpy as np from pycuda import driver, Mar 23, 2015 · CUDA is an excellent framework to start with. props Cuda. I Commonly encountered issues that degrade performance (i. The compiler will produce GPU microcode from your code and send everything that runs on the CPU to your regular compiler. In this article we will use a matrix-matrix multiplication as our main guide. It allows developers to write C++-like code that is executed on the GPU. Another website proclaims that the key is three files: Cuda. There will be P×Q number of threads executing this code. Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. It is incredibly hard to do. Fine-tuning a Code LLM on Custom Code on a single GPU. cu and cuPrintf. /sample_cuda. o object files from your . Disclaimer. Jan 25, 2017 · A quick and easy introduction to CUDA programming for GPUs. is_available(): dev = "cuda:0" else: dev = "cpu" device = torch. You don’t need parallel programming experience. In our case we will need a pointer to the (flattened) big image, an array of (flattened) crop centers coordinates, as well as the image size, the number of channels, the crop size, and the total number of crops. Apr 12, 2020 · Compiling CUDA File in VS Code is not supported in the VS Code natively. To run this part of the code: Use the %%writefile magic command to write the CUDA code into a . As for performance, this example reaches 72. cubin or . To run your Python code on a GPU using CUDA, you can use the `torch. SourceModule: Under "Build Customizations" I see CUDA 3. . Execute the code: ~$ . For more information about error-checking code around calls to the CUDA API, see How to Query Device Properties and Handle Errors in CUDA C/C++ . Best practices for the most important features. Specialized for FP16 TensorCore (NVIDIA GPU) and MatrixCore (AMD GPU) inference. if torch. CUDA programs are C++ programs with additional syntax. is_available(): torch. microsoft. With CUDA, you can leverage a GPU's parallel computing power for a range of high-performance computing applications in the fields of science, healthcare, and deep learning. amp instead of apex/amp now. In this section, we will start learning CUDA programming by writing a very basic program using CUDA C. To run CUDA Python, you’ll need the CUDA Toolkit installed on a system with CUDA-capable GPUs. With these, you can focus on one a single device function or some section of code, at a time. 2\C\src\simplePrintf The CUDA Toolkit includes 100+ code samples, utilities, whitepapers, and additional documentation to help you get started developing, porting, and optimizing your applications for the CUDA architecture. The resultant matrix ( C ) is then printed on the console. cuh from the folder . You could simply demonstrate how to run a sample code like deviceQuery from C#. An OpenCL program consists of two parts: host code and device code. Introduction to CUDA. 2. The following code shows how to request C++ 11 support for the particles target, which means that any CUDA file used by the particles target will be compiled with CUDA C++ 11 enabled (--std=c++11 argument to nvcc). and instead of using a single thread in a for loop to sum the vectors, here we assume we have enough threads executing this kernel to cover every single element in the input vectors - so then we just have to figure out which index (Note that this is an artificial example and you can write such operation just by z = x + y[::-1] without defining a new kernel). multiprocessing import Process, set_start_method import torch import time stream1 = torch. Motivation and Example¶. END OF EDIT. CUDA kernels are atomic functions that are called many times. 2, but when I add kernels to the project they aren't built. This is the only part of CUDA Python that requires some understanding of CUDA C++. Compute Capability We will discuss many of the device attributes contained in the cudaDeviceProp type in future posts of this series, but I want to mention two important fields here, major and minor. cu file. C:\ProgramData\NVIDIA Corporation\NVIDIA GPU Computing SDK 4. o object file and then link it with the . Declare shared memory in CUDA C/C++ device code using the __shared__ variable declaration specifier. We will assume an understanding of basic CUDA concepts, such as kernel functions and thread blocks. Your first kernel will add 2 to each element. Heterogeneous Computing. Publicly available code LLMs such as Codex, StarCoder, and Code Llama are great at generating code that adheres to general programming principles and syntax, but they may not align with an organization’s internal conventions, or be aware of proprietary libraries. 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. For the sake of simplicity, I decided to show you how to implement relatively well-known and straightforward algorithms. CUDA – First Programs “Hello, world” is traditionally the first program we write. This simple CUDA program demonstrates how to write a function that will execute on the GPU (aka "device"). 0 is available as a preview feature. CUDA Programming Model Basics. First Kernel. I have good experience with Pytorch and C/C++ as well, if that helps answering the question. Oct 2, 2018 · The keyword __global__ signals that the function will be compiled by nvcc (NVIDIA compiler, a wrapper around gcc) and run on GPU. Authored by: Maria Khalusova. This repo will show how to run cuda c or cuda cpp code on the google colab platform for free. ptx: Mar 21, 2021 · This function (aka kernel) will run on the GPU device, where you have concepts such as grids, which contain blocks, which in turn contain threads, lots of threads . Writing CUDA kernels. Follow Mar 11, 2021 · In some instances, minor code adaptations when moving from pandas to cuDF are required when it comes to custom functions used to transform data. Text Learn how to build the dataset and classify text using torchtext library. How to run CUDA on Qt Creator The aim is to configure the Qt Creator project properties to run CUDA code. e. Aug 22, 2024 · Step 8: Execute the code given below to check if CUDA is working or not. com/coffeebeforearchFor live content: h This simple CUDA program demonstrates how to write a function that will execute on the GPU (aka "device"). In this introduction, we show one way to use CUDA in Python, and explain some basic principles of CUDA programming. As you can notice, instead of a loop to execute each pair-wise addition sequentially, CUDA allows us to perform all of these operations simultaneously, using N threads in parallel. For this tutorial, we will complete the previous tutorial by writing a kernel function. Mar 10, 2023 · Write CUDA code: You can now write your CUDA code using PyCUDA. Then, you can move it to GPU if you need to speed up calculations. Note: I want each thread of the cuda kernel to calculate one value in the output matrix. Usually these are a few lines inside the program's For loop. Please refer to the code below. 0 which enables researchers with no CUDA experience to write highly efficient GPU code. As far as I know, it is possible to use C++ like stuff within CUDA (esp. Blocks. For general principles and details on the underlying CUDA API, see Getting Started with CUDA Graphs and the Graphs section of the CUDA C Programming Guide. It lets you write GPGPU kernels in C. 1 and 3. CUDA also exposes many built-in variables and provides the flexibility of multi-dimensional indexing to ease programming. Nov 20, 2017 · I am totally new in cuda and I would like to write a cuda kernel that calculates a convolution given an input matrix, convolution (or filter) and an output matrix. This can be a issue if you want to compile and debug (atleast the CPU part of the file as kernel debugging is not currently supported in VS code at the moment. The problem I have is that the processes are not firing. In fact we can pretty easily write a version of the kernel that compiles and runs either as a parallel CUDA kernel on the GPU or as a sequential loop on the CPU. It’s important to be aware that calling __syncthreads() in divergent code is undefined and can lead to deadlock—all threads within a thread block must call __syncthreads() at the same point. Your solution will be modeled by defining a thread hierarchy of grid, blocks, and threads. Use this guide to install CUDA. g. To this end, we write the corresponding CUDA C code, and feed it into the constructor of a pycuda. CUDA is a parallel computing platform and an API model that was developed by Nvidia. - flin3500/Cuda-Google-Colab After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. Sep 30, 2021 · When you need to use custom algorithms, you inevitably need to travel further down the abstraction hierarchy and use NUMBA. Start from “Hello World!” Write and execute C code on the GPU. pitfalls). CONCEPTS. To install Python-CUDA on a Docker container, you can use the `nvidia-docker` command. Before we jump into CUDA Fortran code, those new to CUDA will benefit from a basic description of the CUDA programming model and some of the terminology used. Click: Apr 2, 2020 · To understand this code first you need to know that each CUDA thread will be executing this code independently. autograd . CUDA is a platform and programming model for CUDA-enabled GPUs. Manage GPU memory. CUDA C++ provides keywords that let kernels get the indices of the running threads. Jan 24, 2020 · Save the code provided in file called sample_cuda. Stream() stream2 = torch Dec 24, 2018 · EDIT. The grid-stride loop code is more like the original sequential loop code than the monolithic kernel code, making it clearer for other users. So far you should have read my other articles about starting with CUDA, so I will not explain the "routine" part of the code (i. everything not relevant to our discussion). As in Sep 12, 2021 · There is another problem with writing CUDA kernels. Aug 10, 2016 · It's a non-trivial task to convert a program from straight C(++) to CUDA. The following code block shows how you can assign this placement. cu -o sample_cuda. Aug 1, 2017 · To make target_compile_features easier to use with CUDA, CMake uses the same set of C++ feature keywords for CUDA C++. If you are being chased or someone will fire you if you don’t get that op done by the end of the day, you can skip this section and head straight to the implementation details in the next section. If you want to test it, user a lower value for upper_bound. Dec 9, 2018 · This repository contains a tutorial code for making a custom CUDA function for pytorch. To see how it works, put the following code in a file named hello. To avoid misleading people, as M. A couple of additional notes: You don't need to compile your . 0), but I think it's easier to start with only C stuff (i. The following issues are still unresolved and I still hunting for solutions: The auto-complete feature for threads and block dimensions is not working. The CPU, or "host", creates CUDA threads by calling special functions called "kernels". The CUDA programming model is a heterogeneous model in which both the CPU and GPU are used. If you want to go further, you could try and implement the gaussian blur algorithm to smooth photos on the GPU. CUDA work issued to a capturing stream doesn’t actually run on the GPU. Jun 29, 2023 · When accessing arrays in CUDA, use a grid-stride loop to write code for arbitrarily sized arrays. You (probably) need experience with C or C++. GPU programming is complicated. Manage communication and synchronization. cu: This is the third and final tutorial on doing “NLP From Scratch”, where we write our own classes and functions to preprocess the data to do our NLP modeling tasks. Goals Our goals in this section are I Understand the performance characteristics of GPUs. The cuda code is mainly for nvidia hardware device. CUDA has an execution model unlike the traditional sequential model used for programming CPUs. Jul 14, 2016 · The CUDA profiler is rather crude and doesn't provide a lot of useful information. to Jan 1, 2013 · One way of solving this problem is by using cuPrintf function which is capable of printing from the kernels. The time to set up the additional oneAPI for NVIDIA GPUs was about 10 minutes on In this video we look at the basic setup for CUDA development with VIsual Studio 2019!For code samples: http://github. 3. This course will help prepare students for developing code that can process large amounts of data in parallel on Graphics Processing Units (GPUs). Writing a CUDA Kernel. The MEX function contains the host-side code that interacts with gpuArray objects from MATLAB ® and launches the CUDA code. The procedure to do that is fairly simple. When you are porting or writing new CUDA C/C++ code, I recommend that you start with pageable transfers from existing host pointers. The images that follow show what your code should generate assuming you convert your code to CUDA correctly. Profiling Mandelbrot C# code in the CUDA source view. from torch. Mar 12, 2024 · There is no need to modify anything in the code, except the body of the CUDA all_primes_to inside the check_prime_gpu_code string, as we did in the examples so far. Finally, such as in the CUDA programming model, the host communicates with the device(s) through the global memory of the device(s). Compile the code: ~$ nvcc sample_cuda. This book covers the following exciting features: Oct 18, 2018 · When writing vector quantities or structures in C/C++, care should be taken to ensure that the underlying write (store) instruction in SASS code references the appropriate size. Set Up CUDA Python. cudlaCreateDevice creates the DLA device. , thecode is not executed inside the processes. ”Although a variety of systems have recently emerged to make this process easier, we have found them to be either too verbose, lack flexibility or generate code noticeably slower than our hand-tuned Sign up for Pure Virtual C++ 2021 today! https://visualstudio. use numba+CUDA on Google Colab; write your first custom CUDA kernels, to process 1D or 2D data. Python-CUDA is a library that allows you to use NVIDIA GPUs to accelerate your Python code. 1 toolkit, converting that CUDA C++ kernel definition to an equivalent PTX kernel definition via nvcc -ptx vectorAdd_kernel. As the name suggests, the host code is executed by the host and also "submits the kernel code as commands from the host to OpenCL devices". Jun 23, 2020 · The C# part. cuDF uses Numba to convert and compile the Python code into a CUDA kernel Now announcing: CUDA support in Visual Studio Code! With the benefits of GPU computing moving mainstream, you might be wondering how to incorporate GPU com Aug 7, 2020 · Here is the code as a whole if-else statement: torch. Use !nvcc to compile the code. We will use CUDA runtime API throughout this tutorial. device('cpu') Since you probably want to store the device for later, you might want something like this instead: We could extend the above code to print out all such data, but the deviceQuery code sample provided with the NVIDIA CUDA Toolkit already does this. The indexing operator y[_ind. It’s common practice to write CUDA kernels near the top of a translation unit, so write it next. In this video I introduc Multiple examples of CUDA/HIP code are available in the content/examples/cuda-hip directory of this repository. Nov 5, 2018 · You should be able to take your C++ code, add the appropriate __device__ annotations, add appropriate delete or cudaFree calls, adjust any floating point constants and plumb the local random state as needed to complete the translation. Using CUDA, one can utilize the power of Nvidia GPUs to perform general computing tasks, such as multiplying matrices and performing other linear algebra operations, instead of just doing graphical calculations. It has bindings to CUDA and allows you to write your own CUDA kernels in Python. cu to indicate it is a CUDA code. device('cuda' if torch. is_available() else 'cpu') if torch. Improve this question. CUDA is a software platform developed by NVIDIA that allows us to write and execute code on NVIDIA GPUs. The primary cuDLA APIs used in this YOLOv5 sample are detailed below. Also note, that we recommend to use the native mixed-precision training utility via torch. CUDA CUDA is a parallel computing platform and API developed by NVIDIA. Apr 20, 2024 · On this page, we will take a look at what happens under the hood when you run a PyTorch operation on a GPU, and explore the basic tools and concepts you need to write your own custom GPU operations for PyTorch. ) Jul 11, 2009 · Welcome to the second tutorial in how to write high performance CUDA based applications. With Colab, you can work with CUDA C/C++ on the GPU for free. This is 83% of the same code, handwritten in CUDA C++. The file extension is . I have seen CUDA code and it does seem a bit intimidating. cuda. We write our own custom autograd function for computing forward and backward of \(P_3\), and use it to implement our model: # -*- coding: utf-8 -*- import torch import math class LegendrePolynomial3 ( torch . Using cuDLA standalone mode can prevent the creation of CUDA context, and thus can save resources if the pipeline has no CUDA context. Aug 6, 2021 · Last month, OpenAI unveiled a new programming language called Triton 1. I provide lots of fully worked examples in my answers, even ones that include things like OpenMP and calling CUDA code from python. structs, pointers, elementary data types). device('cuda') else: torch. 5% of peak compute FLOP/s. cu to a . Dec 26, 2020 · You could use AT_DISPATCH_FLOATING_TYPES_AND_HALF to dispatch the code for the float16 type and use scalar_t in the code (similar to e. The string is compiled later using NVRTC. CUDA also manages different memories including registers, shared memory and L1 cache, L2 cache, and global memory. Tibbits points out printf is available in any GPU of compute capability 2. is_available()` function. You don’t need GPU experience. As I mentioned earlier, as you write more device code you will eliminate some of the intermediate transfers, so any effort you spend optimizing transfers early in porting may be wasted. Dec 27, 2022 · Conclusion. Here it is: In file hello. ptx file. targets, but it doesn't say how or where to add these files -- or rather I'll gamble that I just don't understand the notes referenced in the website. 0, an open-source Python-like programming language which enables researchers with no CUDA experience to write highly efficient GPU code—most of the time on par with what an expert would be able to produce. Jun 26, 2020 · CUDA code also provides for data transfer between host and device memory, over the PCIe bus. The following adds two vectors together. h> Run the compiled CUDA file created in the last step. Aug 31, 2023 · In short, using cuDLA hybrid mode can give quick integration with other CUDA tasks. Before going into the details of code, one thing that you should recall is that host code is compiled by the standard C compiler and that the device code is executed by Jan 2, 2024 · For this tutorial, we’ll stick to something simple: We will write code to double each entry in a_gpu. Any suggestions/resources on how to get started learning CUDA programming? Quality books, videos, lectures, everything works. Prerequisites. The calls are cudaProfilerStart() and cudaProfilerStop(). The entire kernel is wrapped in triple quotes to form a string. Write better code with AI Code review. You can check out CUDA zone to see what can be Sep 29, 2022 · Programming environment. xml Cuda. The cudaMallocManaged(), cudaDeviceSynchronize() and cudaFree() are keywords used to allocate memory managed by the Unified Memory May 29, 2021 · To start with, we need an appropriate PTX kernel definition. It is NVIDIA only though and only works on 8-series cards or better. It is historically the first mainstream GPU programming framework. Run the compiled executable with !. But before we can run this code, we need to do another modification. cu: Write MEX File Containing CUDA Code All MEX files, including those containing CUDA ® code, have a single entry point known as mexFunction . cu. with the announced CUDA 4. In this second post we discuss how to analyze the performance of this and other CUDA C/C++ codes. We will start by writing a Hello, CUDA! program in CUDA C and execute it. Be aware that the provided CUDA code is a direct port of the Python code, and therefore very slow. For more information, see An Even Easier Introduction to CUDA. The aim of this article is to learn how to write optimized code on GPU using both CUDA & CuPy. size()-i-1] involves an indexing computation on y, so y can be arbitrarily shaped and strode. Jun 3, 2019 · CUDA is NVIDIA's parallel computing architecture that enables dramatic increases in computing performance by harnessing the power of the GPU. Copy the files cuPrintf. This tutorial is an introduction for writing your first CUDA C program and offload computation to a GPU. Now we are ready to run CUDA C/C++ code right in your Notebook. 0 and higher. In the first post of this series we looked at the basic elements of CUDA C/C++ by examining a CUDA C/C++ implementation of SAXPY. It will learn on how to implement software that can solve complex problems with the leading consumer to enterprise-grade GPUs available using Nvidia CUDA. While cuBLAS and cuDNN cover many of the potential uses for Tensor Cores, you can also program them directly in CUDA C++. !nvcc --version Five steps to write your first program Nov 3, 2014 · I am writing a simpled code about the addition of the elements of 2 matrices A and B; the code is quite simple and it is inspired on the example given in chapter 2 of the CUDA C Programming Guide. It's possible to instrument the code to select a section to profile. You have choices: I used to find writing CUDA code rather terrifying. Create a new Notebook. A raw argument can be used like an array. In your project, hit F5F5/F5 and you'll get the below pop-up. I wanted to get some hands on experience with writing lower-level stuff. – Apr 23, 2020 · To check, if you successfully installed CUDA in notebook you can write the following code to check the version. h" Insert hello world code into the file. The code samples covers a wide range of applications and techniques, including: Simple techniques demonstrating. cu: #include "stdio. You need to compile it to a . Samples for CUDA Developers which demonstrates features in CUDA Toolkit - NVIDIA/cuda-samples. more. For this, we will be using either Jupyter Notebook, a programming Oct 17, 2017 · Access to Tensor Cores in kernels through CUDA 9. Before we jump into CUDA C code, those new to CUDA will benefit from a basic description of the CUDA programming model and some of the terminology used. The comments above when referring to write operations are referring to the writes as issued by the SASS code. I Best practice for obtaining good performance. iometc duhhno iwhqq aaor vbbi yoprh lijqpvg vlwlrd apfznsk baghr