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Author: aseembits93

docs/codeflash-concepts/benchmarking-gpu-code.mdx

@@ -110,8 +110,8 @@ With synchronize: 152.277 ms
 
 # How Codeflash measures execution time involving GPUs
 
-Codeflash automatically inserts synchronization barriers before measuring performance. It currently supports GPU code written in `Pytorch`, `Tensorflow` and `JAX` for NVIDIA GPUs (CUDA) and MacOS Metal Performance Shaders (MPS).
+Codeflash automatically inserts synchronization barriers before measuring performance. It currently supports GPU code written in `Pytorch`, `Tensorflow` and `JAX` for NVIDIA GPUs (`CUDA`) and MacOS Metal Performance Shaders (`MPS`).
 
-- **PyTorch**: Uses `torch.cuda.synchronize()` (CUDA) or `torch.mps.synchronize()` (MPS) depending on the device.
-- **JAX**: Uses `jax.block_until_ready()` to wait for computation to complete. It works for both CUDA and MPS devices.
-- **TensorFlow**: Uses `tf.test.experimental.sync_devices()` for device synchronization. It works for both CUDA and MPS devices.
+- **PyTorch**: Uses `torch.cuda.synchronize()` (`CUDA`) or `torch.mps.synchronize()` (`MPS`) depending on the device.
+- **JAX**: Uses `jax.block_until_ready()` to wait for computation to complete. It works for both `CUDA` and `MPS` devices.
+- **TensorFlow**: Uses `tf.test.experimental.sync_devices()` for device synchronization. It works for both `CUDA` and `MPS` devices.