Strided vs Unstrided Memory Access for GPU Computing

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Now, if you’re not familiar with these terms, let me break it down for ya: when your data doesn’t fit neatly into a contiguous block of memory (which is pretty common in deep learning), you have to use some fancy techniques to access that data efficiently on the GPU.

Strided memory access involves jumping over certain elements in an array, while unstrided memory access just reads or writes every element sequentially. The question is: which one should you choose? Well, let’s take a look at some examples!

First up, we have strided memory access. This technique can be useful when working with sparse data (i.e., not all elements are used), because it allows us to skip over the unused ones and save time. For example: 

# Load a sparse matrix into GPU memory using PyTorch's SparseTensor class
# The following line creates a sparse matrix using the indices and values provided
sparse_matrix = torch.sparse.FloatTensor(indices, values)

# Compute dot product with another dense matrix (using strided access to avoid unnecessary reads)
# The following line creates a dense vector of random values and moves it to the GPU
dense_vector = torch.randn(1024).cuda()

# The following line performs a dot product between the sparse matrix and the dense vector
# It uses strided access to only read the necessary elements, saving time and resources
output = sparse_matrix * dense_vector[sparse_matrix.indices]

In this example, we’re using PyTorch’s SparseTensor class to load a sparse matrix into GPU memory (which is much more efficient than storing it as a dense array). Then, instead of reading every element in the matrix and multiplying them with the corresponding elements in another dense vector, we’re only accessing the ones that are actually needed. This can result in significant speedups for large datasets!

On the other hand, unstrided memory access is better suited for working with contiguous data (i.e., all elements are used). For example: 

# Load a dense matrix into GPU memory using PyTorch's Tensor class
dense_matrix = torch.randn(1024, 1024).cuda() # Creates a 1024x1024 matrix of random values and moves it to the GPU for faster computation

# Create a dense vector with the same number of elements as the matrix
dense_vector = torch.randn(1024).cuda()

# Compute dot product with another dense matrix (using strided access for optimal performance)
output = dense_matrix @ dense_vector # Performs matrix multiplication between the matrix and vector, resulting in a 1024x1 vector

# Print the result
print(output)

In this example, we’re using PyTorch’s Tensor class to load a dense matrix into GPU memory. Then, instead of jumping over certain elements in the matrix and multiplying them with corresponding elements from another sparse vector (which would be slower due to strided access), we’re just reading every element sequentially and computing their dot product using PyTorch’s built-in @ operator. This can result in even faster performance for large datasets!

So, which one should you choose? Well, it depends on your specific use case! If you have sparse data that doesn’t fit neatly into a contiguous block of memory (which is pretty common in deep learning), then strided memory access can be useful. But if you have dense data that does fit neatly into a contiguous block of memory, then unstrided memory access can be faster and more efficient!

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