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To optimize parallelism in your data mining pipeline, you need to understand the characteristics and requirements of your data, such as the type, size, structure, format, quality, and distribution.
Hi, thanks! I use vllm to inference the llama-7B model on single gpu, and tensor-parallel on 2-gpus and 4-gpus, we found that it is 10 times faster than HF on a single GPU, but using tensor ...
Parallelism methods. Multi-GPU setups are effective for accelerating training and fitting large models in memory that otherwise wouldn't fit on a single GPU. It relies on parallelizing the workload ...
Colossal-AI is a large-scale deep learning model designed to train data parallelly. It combines different standards of parallelization techniques such as pipeline parallelism, data parallelism, tensor ...
Parallel primitives: A unified framework to describe parallel strategies. Mainstream training systems, such as Megatron-LM, DeepSpeed, and Alpa, typically incorporate built-in parallel strategies like ...
Real-time stream processing applications such as software defined radios are usually executed concurrently on multiprocessor systems. Exploiting coarse-grained data parallelism by duplicating tasks is ...
To speed up computation, deep neural networks (DNNs) usually rely on highly optimized tensor operators. Despite the effectiveness, tensor operators are often defined empirically with ad hoc semantics.
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