シャドーイング練習: Nvidia CUDA in 100 Seconds - 動画で英語スピーキングを学ぶ
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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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このビデオで身につく英語スピーキングのスキル
このビデオを使った英語スピーキング練習では、専門用語の発音や速いスピードでのリスニング、説明的な文章のリズムを磨くことができます。特にIELTSスピーキング対策にも役立つ、技術的な内容を明確に伝える能力が向上します。
聞き取りに注意すべき発音・リンキング
ネイティブスピーカーの発音では、以下のような繋がりや短縮形がよく見られます。「CUDA kernel」では「kernel」の「k」が軽くなり、「GPU's memory」では「GPU's」と「memory」が滑らかに繋がります。また、「trillions of」は「tri llions of」ではなく「trillionsa」のように発音されることがあります。これらのリンキングを覚えると、リスニングとスピーキングの自然さが一気に上がります。
ネイティブのように話すコツ
ビデオのスピーカーは、技術的な内容を分かりやすく伝えるために、重要な単語を強調し、リズムを抑えて話しています。例えば「revolutionized the world」では「revolutionized」を強調し、「16,000 cores」では数字をはっきりと発音しています。shadowing practice（シャドーイング練習）をする際は、このリズムと強調を真似ることがポイントです。また、「Piece of cake」のような慣用句は、自然なアクセントで発音することで、会話に活気を与えます。
実践的な練習法
- ビデオを10秒ずつ止めながら、シャドーイングを繰り返しましょう。特にリンキングの部分を注意深く真似ます。
- 「shadowspeak」や「shadowing site」を使って、自分の発音を録音し、ネイティブと比較します。
- IELTSスピーキング対策として、ビデオの内容を簡単にまとめて話す練習をしましょう。これで技術的なトピックの説明能力が向上します。
これらの練習を続けると、英語スピーキングの自然さと正確性が大幅に向上し、ネイティブとのコミュニケーションもスムーズになります。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。