What is Wavelet Distributed Training?
Wavelet distributed training is an approach to neural network training that uses an asynchronous data parallel technique to divide the training tasks into two waves. The tick-wave and tock-wave run on the same group of GPUs and are interleaved so that each wave can leverage the on-device memory of the other wave during their memory valley period.
How does Wavelet work?
Wavelet divides dataparallel training tasks into two waves, tick-wave and tock-wave. The task launching offset is achieved by delaying the launch time of tock-wave tasks for half of a whole forward-backward training cycle. This allows the tock-wave tasks to directly leverage GPU memory valley period of tick-wave tasks, since backward propagation of tick-wave tasks is compute-heavy but memory is often unused. Similarly, tick-wave tasks can leverage memory valley period of tock-wave tasks in the same way.
The Benefits of Wavelet Distributed Training
Wavelet distributed training provides the following benefits:
Increased Throughput
Wavelet expands the computing power of the GPU by using it more efficiently during the training period, resulting in a faster training time. By interleaving the tick-wave and tock-wave tasks, wavelet distributes the workload more evenly, which further speeds up the training process. This increased throughput time is a significant benefit of wavelet distributed training.
Better Utilization of GPU Memory
During training, backward propagation tasks are compute-heavy but memory is often unused. By using the memory valley period of tick-wave tasks, tock-wave tasks can leverage on-device memory to increase the efficiency of the memory usage during training. Similarly, tick-wave tasks can do the same by utilizing the memory valley period of tock-wave tasks. This results in better utilization of the GPU memory and an improved training time.
Scalability
Wavelet distributed training is highly scalable as it can be used in a distributed environment. It enables the training of deep neural networks on large datasets, which is time-consuming and computationally expensive. Wavelet takes advantage of multiple GPUs in parallel, allowing for increased throughput and scalability.
Applications of Wavelet Distributed Training
Wavelet distributed training has several important applications in various industries. Some of these applications include:
Speech Recognition
Wavelet distributed training has been used for speech recognition applications. By using wavelet, researchers have been able to accelerate the training of acoustic models with improved accuracy, leading to better speech recognition.
Computer Vision
Wavelet distributed training has been applied to computer vision applications, such as object detection and image recognition. By using wavelet, researchers were able to train deep neural networks faster and with better accuracy than traditional data-parallel training methods.
Natural Language Processing
Wavelet distributed training has been used for natural language processing applications. Researchers have used it to train language models that can understand natural language processing procedures with high accuracy.
Wavelet distributed training is a more efficient method of training deep neural networks using GPUs. It maximizes the use of available memory and provides higher throughput than traditional data-parallel training methods. Wavelet distributed training has many potential applications in various industries, including speech recognition, computer vision, and natural language processing.
