You’ve heard about how awesome GPUs are for machine learning and you want to optimize your memory allocation for large scale applications.

First, why memory allocation is important. In traditional CPU-based computing, you can just allocate as much RAM as your application needs and call it a day. But when we’re dealing with GPUs, things get a little more complicated. You see, GPUs have limited onboard memory (called GDDR5 or GDDR6) that is shared among all the cores. This means you need to be strategic about how you allocate your memory if you want to avoid running out of space and causing performance issues.

So Let’s jump right into some tips for optimizing GPU memory allocation in large scale applications:

1. Use pinned memory Pinned memory is a special type of memory that can be accessed directly by the CPU without going through the operating system’s virtual memory manager. This means you can avoid the overhead associated with page faults and improve your application’s performance. To use pinned memory in Python, you can create an array using `numpy` or `cupy`, then pin it to a specific location in physical memory using `cuda.pin_memory()`.

2. Use CUDA arrays Instead of copying data between CPU and GPU memory every time your application needs to access it, consider storing the data on the GPU itself using CUDA arrays. This can significantly reduce the amount of memory bandwidth required by your application and improve its overall performance. To create a CUDA array in Python, you can use `cupy` or write some low-level C/C++ code to allocate and manage the memory yourself.

3. Use shared memory Shared memory is a small amount of onboard memory that is accessible by all cores within a single GPU block. By storing frequently accessed data in shared memory, you can reduce the number of times your application needs to access global memory (which is much slower) and improve its overall performance. To use shared memory in Python, you’ll need to write some low-level C/C++ code or use a library like `cupy` that provides high-level APIs for managing shared memory.

4. Use constant memory Constant memory is a special type of onboard memory that can be accessed by all cores within a single GPU block without any overhead associated with loading the data from global memory. By storing frequently accessed constants in constant memory, you can improve your application’s performance and reduce its overall memory footprint. To use constant memory in Python, you’ll need to write some low-level C/C++ code or use a library like `cupy` that provides high-level APIs for managing constant memory.

5. Use texture memory Texture memory is another special type of onboard memory that can be accessed by all cores within a single GPU block without any overhead associated with loading the data from global memory. By storing frequently accessed textures in texture memory, you can improve your application’s performance and reduce its overall memory footprint. To use texture memory in Python, you’ll need to write some low-level C/C++ code or use a library like `cupy` that provides high-level APIs for managing texture memory.

6. Use tiled memory Tiled memory is a technique used by modern GPUs to improve their performance when accessing large datasets. By breaking the data into smaller tiles and storing them in onboard memory, you can reduce the amount of time it takes to load the data from global memory and improve your application’s overall performance. To use tiled memory in Python, you’ll need to write some low-level C/C++ code or use a library like `cupy` that provides high-level APIs for managing tiled memory.

7. Use asynchronous memory transfers Asynchronous memory transfers allow your application to continue executing while data is being transferred between CPU and GPU memory. This can significantly reduce the amount of time it takes to load or save large datasets and improve your application’s overall performance. To use asynchronous memory transfers in Python, you’ll need to write some low-level C/C++ code or use a library like `cupy` that provides high-level APIs for managing asynchronous memory transfers.

8. Use lazy loading Lazy loading is a technique used by modern GPUs to improve their performance when accessing large datasets. By only loading the data into onboard memory when it’s actually needed, you can reduce the amount of time it takes to load the data from global memory and improve your application’s overall performance. To use lazy loading in Python, you’ll need to write some low-level C/C++ code or use a library like `cupy` that provides high-level APIs for managing lazy loading.

9. Use compressed textures Compressed textures are another technique used by modern GPUs to improve their performance when accessing large datasets. By compressing the data into smaller formats, you can reduce the amount of time it takes to load the data from global memory and improve your application’s overall performance. To use compressed textures in Python, you’ll need to write some low-level C/C++ code or use a library like `cupy` that provides high-level APIs for managing compressed textures.

10. Use sparse data structures Sparse data structures are another technique used by modern GPUs to improve their performance when accessing large datasets with many missing values. By storing only the non-zero elements in onboard memory, you can reduce the amount of time it takes to load the data from global memory and improve your application’s overall performance. To use sparse data structures in Python, you’ll need to write some low-level C/C++ code or use a library like `cupy` that provides high-level APIs for managing sparse data structures.

11. Use GPUDirect GPUDirect is a technique used by modern GPUs to improve their performance when transferring large datasets between CPU and GPU memory without involving the operating system’s virtual memory manager. By using this technique, you can reduce the amount of time it takes to load or save large datasets and improve your application’s overall performance. To use GPUDirect in Python, you’ll need to write some low-level C/C++ code or use a library like `cupy` that provides high-level APIs for managing GPUDirect transfers.