쉐도잉 연습: Nvidia CUDA in 100 Seconds - 영상으로 영어 말하기 배우기
레슨 만드는 중...
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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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이 레슨에서 연습할 내용
이 비디오는 "Nvidia CUDA"에 대한 설명으로, 기술 용어와 병렬 컴퓨팅 개념을 포함한 영어 발화를 연습할 수 있습니다. 빠른 속도의 설명과 전문 용어를 이해하며, 영어 쉐도잉을 통해 발음과 리듬을 개선하고, IELTS 스피킹에서 요구되는 명확한 표현 능력을 기르실 수 있습니다.
주요 어휘 및 구문
- Parallel computing: 병렬 컴퓨팅 (여러 작업을 동시에 처리하는 컴퓨팅 방식)
- GPU (Graphics Processing Unit): 그래픽 처리 장치 (그래픽 외에도 병렬 연산에 사용됨)
- CUDA kernel: CUDA 커널 (GPU에서 실행되는 함수)
- Teraflops: 테라플롭스 (초당 1조 번의 부동소수점 연산 능력)
- Deep neural networks: 심층 신경망 (인공지능의 기본 구조)
연습 팁
이 비디오는 기술적 내용이 많아 발음이 명확하지만 속도가 빠릅니다. 영어 쉐도잉을 할 때는 다음과 같이 연습하세요. 먼저 비디오를 0.75배 속도로 재생하며, 발화자와 같은 톤과 강세로 따라합니다. 특히 "matrix multiplication"이나 "vector transformations"와 같은 복합 단어는 음절을 분리하여 발음하는 연습을 하세요. 이는 영어 발음 교정에 도움이 됩니다. 두 번째로, 키워드가 등장하는 부분을 반복해서 듣고, 스스로 설명해 보세요. 예를 들어 "CUDA allows developers to tap into the GPU's power"를 바탕으로 자신의 말로 다시 표현해 보면, IELTS 스피킹에서 요구되는 유창성이 향상됩니다. 마지막으로, shadowing site를 이용하여 녹음한 자신의 발화와 원본을 비교해 보세요. 리듬과 강세의 차이를 찾고, 반복해서 수정하면 더 자연스러운 발화가 가능해집니다.
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.