تدريب Shadowing: Nvidia CUDA in 100 Seconds - تعلم التحدث بالإنجليزية عبر الفيديو
جارٍ إنشاء الدرس...
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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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الانطباعات الأولى
يعتبر الفيديو مثاليًا كمادة تعليمية لتعلم الإنجليزية بسبب طابعه الديناميكي والمليء بالمعلومات القيمة. يتحدث المتحدث بطلاقة وثقة، مما يسهل على المتعلمين متابعة المحادثة وفهم السياق. يركز الحوار على موضوع حديث ومثير يتعلق بتكنولوجيا CUDA، مما يجعله جذابًا للمهتمين بالمجالات التقنية. كما أن المحتوى مصاغ بلغة بسيطة ومباشرة، مما يسهل على المتعلمين الجدد فهمه واستخدامه في محادثاتهم اليومية.
تحليل الجمل
لنأخذ بعض الجمل من الحوار ونحللها لنفهم لماذا تبدو طبيعية:
- "CUDA allows developers to tap into the GPU's power." - هذه الجملة توضح الفائدة الرئيسية لـ CUDA بطريقة مباشرة، مما يسهل على المتعلم فهم القيمة المقصودة.
- "You write a function, called a CUDA kernel, that runs on the GPU." - استخدام كلمة "called" يضيف شفافية للجملة ويجعلها أكثر وضوحًا.
- "The code is executed in a block, which itself organizes threads into a multi-dimensional grid." - توضيح كيفية تنفيذ الكود بطريقة مرئية يجعل المعلومات التقنية أقل تعقيدًا.
تتميز الجمل بإيقاع طبيعي وبنية واضحة، مما يسهل على المتعلمين تحسين النطق باللغة الإنجليزية باستخدام طريقة التظليل في الإنجليزية.
روتين الممارسة
لتعزيز مهارات النطق والاستماع، يمكنك اتباع روتين بسيط يعتمد على تقنية shadowspeak:
- شغل الفيديو واختر جزءًا قصيرًا لا يتجاوز 30 ثانية.
- استمع إلى الجمل بتركيز، وحاول فهم المعاني والمفردات المستخدمة.
- قم بتكرار الجمل بعد المتحدث مباشرة، مع محاولة تقليد النبرة والإيقاع.
- سجل صوتك أثناء التكرار واستمع إليه لاحقًا لتقييم أدائك وتحسينه.
باستخدام هذه الطريقة، يمكنك تحسين نطقك وثقتك في التحدث باللغة الإنجليزية، مما يسهل عليك تعلم الإنجليزية مع يوتيوب بشكل فعال. اجعل هذه الممارسة جزءًا من روتينك اليومي لتحقيق أفضل النتائج.
ما هي تقنية التظليل الصوتي؟
التظليل الصوتي (Shadowing) تقنية تعلم لغة مدعومة علمياً، طُورت أصلاً لتدريب المترجمين الفوريين المحترفين. الطريقة بسيطة لكنها قوية: تستمع لصوت إنجليزي أصلي وتكرره فوراً بصوت عالٍ — كظل يتبع المتحدث بتأخير 1-2 ثانية. تُظهر الأبحاث تحسناً كبيراً في دقة النطق والتنغيم والإيقاع وربط الأصوات والاستماع والطلاقة.