Практика Shadowing: Nvidia CUDA in 100 Seconds - Изучайте разговорный английский по видео
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CUDA, a parallel computing platform that allows you to use your GPU for more than just playing video games.
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Compute Unified Device Architecture was developed by NVIDIA in 2007 based on the prior work of Ian Buck and John Nichols.
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Since then, CUDA has revolutionized the world by allowing humans to compute large blocks of data in parallel, which has unlocked the true potential of the deep neural networks behind artificial intelligence.
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The Graphics Processing Unit, or GPU, is historically used for what the name implies, to compute graphics.
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When you play a game in 1080p at 60fps, you've got over 2 million pixels on the screen that may need to be recalculated after every frame,
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which requires hardware that can do a lot of matrix multiplication and vector transformations in parallel.
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And I mean a lot.
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Modern GPUs are measured in teraflops, or how many trillions of floating point operations can it handle per second?
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Unlike modern CPUs like the Intel i9, which has 24 cores, a modern GPU like the RTX 4090 has over 16,000 cores.
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A CPU is designed to be versatile, while a GPU is designed to go really fast in parallel.
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CUDA allows developers to tap into the GPU's power, and data scientists all around the world are using at this very moment, trying to train the most powerful machine learning models.
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It works like this.
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You write a function, called a CUDA kernel, that runs on the GPU.
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You then copy some data from your main RAM over to the GPU's memory, then the CPU will tell the GPU to execute that function or kernel in parallel.
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The code is executed in a block, which itself organizes threads into a multi-dimensional grid.
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Then the final result from the GPU is copied back to the main memory.
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Piece of cake, let's go ahead and build a CUDA application right now.
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First you'll need an NVIDIA GPU, then install the CUDA toolkit.
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CUDA includes device drivers, runtime, compilers, and dev tools, but the actual code is most often written in C++, as I'm doing here in Visual Studio.
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First, we use the global specifier to define a function or CUDA kernel that runs on the actual GPU.
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This function adds two vectors or arrays together.
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It takes pointer arguments A and B, which are the two vectors to be added together, and pointer C for the result.
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C equals A plus B, but because hypothetically we're doing billions of operations in parallel, we need to calculate the global index of the thread in the block that we're working on.
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From there, we can use managed, which tells CUDA this data can be accessed from both the host CPU and the device GPU, without the need to manually copy data between them.
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And now we can write a main function for the CPU that runs the CUDA kernel.
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We use a for loop to initialize our arrays with data, then from there, we pass this data to the add function to run it on the GPU.
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But you might be wondering what these weird triple brackets are.
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They allow us to configure the CUDA kernel launch to control how many blocks
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and how many threads per block are used to run this code in parallel.
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And that's crucial for optimizing multi-dimensional data structures like tensors used in deep learning.
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From there, CUDA Device Synchronize will pause the execution of this code and wait for it to complete on the GPU.
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When it finishes and copies the data back to the host machine, we can then use the result and print it to the standard output.
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Now, let's execute this code with a CUDA compiler by clicking the play button.
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Congratulations, you just ran 256 threads in parallel on your GPU.
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But if you want to go beyond, NVIDIA's GTC conference is coming up in a few weeks.
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It's free to attend virtually, featuring talks about building massive parallel systems with CUDA.
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Thanks for watching, and I will see you in the next one.
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Почему практиковать речь на этом видео?
Видео о NVIDIA CUDA — отличная возможность улучшить произношение английского и научиться говорить уверенно на технические темы. Он содержит ясную, структурированную речь с яркими примерами, что идеально подходит для метода shadowspeak (или shadow speak). Ты сможешь повторять за спикером, тренировать ритм и интонацию, а также запоминать полезные фразы для объяснения сложных концепций. Учиться английский с YouTube стало еще эффективнее — это быстрые победы, которые поднимут твой уровень!
Грамматика и выражения в контексте
- "allows you to use" — конструкция с инфинитивом, чтобы выразить возможность. Практикуй: "This tool allows me to solve problems faster."
- "developed by NVIDIA in 2007 based on" — пассивный залог с указанием автора и основы. Учась так говорить, ты сможешь описывать создания: "The app was developed by a team based on user feedback."
- "which has unlocked the true potential" — относительный предложение с Present Perfect, чтобы показать результат. Используй: "The new technology, which has unlocked many opportunities, is changing the industry."
Трудные места для произношения
На видео есть слова, которые могут сбить с толку. Например, CUDA произносится как [ˈkjuːdə], а не "куда". teraflops — [ˈterəflɒps], акцент на первом слоге. Также обращай внимание на parallel ([ˈpærəlel]) — не путай с "параллель" на русском. Используй shadowing site, чтобы повторять эти слова до идеала. Тренируйся каждый день, и soon ты будешь говорить их без усилий!
Что такое техника Shadowing?
Shadowing — это научно обоснованная техника изучения языка, изначально разработанная для подготовки профессиональных переводчиков и популяризированная полиглотом доктором Александром Аргуэльесом. Метод прост, но эффективен: вы слушаете аудио на английском от носителей языка и немедленно повторяете вслух — как тень, следующая за говорящим с задержкой в 1–2 секунды. В отличие от пассивного прослушивания или грамматических упражнений, Shadowing заставляет мозг и мышцы рта одновременно обрабатывать и воспроизводить реальные речевые паттерны. Исследования показывают, что это значительно улучшает точность произношения, интонацию, ритм, связную речь, понимание на слух и беглость речи — что делает его одним из самых эффективных методов для подготовки к IELTS Speaking и реального общения на английском.