This is the Linux app named BlockSparse whose latest release can be downloaded as blocksparsev1.0.0sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named BlockSparse with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start the OnWorks Linux online or Windows online emulator or MACOS online emulator from this website.
- 5. From the OnWorks Linux OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application, install it and run it.
SCREENSHOTS
Ad
BlockSparse
DESCRIPTION
The blocksparse repository provides efficient GPU kernels (TensorFlow custom ops) for block-sparse matrix multiplication and convolution operations. The idea is to exploit block-level sparsity — i.e. treat matrices or weight tensors as composed of blocks, many of which may be zero or unused — to save compute and memory when sparsity patterns are structured. This is particularly useful in models like Sparse Transformers, where attention matrices or intermediate layers may adopt block-sparse patterns to scale better. The repo implements both blocksparse and blockwise convolution/transpose-convolution primitives, with support for preparing, executing, and verifying those ops on NVIDIA GPUs. In addition to low-level kernels, it includes wrapper code for integrating with TensorFlow, example scripts (e.g. a transformer on the enwik8 dataset), transformer logic that uses blocksparse operations, and debugging helpers.
Features
- Blocksparse matrix multiplication kernels tuned for GPU execution
- Blocksparse convolution and transpose-convolution (deconvolution) primitives
- TensorFlow custom ops / wrappers for easy integration into TF graphs
- Example transformer model using block-sparse ops (e.g. enwik8 script)
- Support utilities: shape helpers, edge-bias, normalization, bias operations
- Verification, testing, and debugging support to validate correctness of sparse execution
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/blocksparse.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
