Part 1. Definition
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Part 2. Example
Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.
Answer: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Explanation: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.
Part 3. Exercise
In recent years, the efficiency and even the feasibility of traditional load-balancing policies are challenged by the rapid growth of cloud infrastructure with increasing levels of server heterogeneity and increasing size of cloud services and applications. In such many software-load-balancers heterogeneous systems, traditional solutions, such as JSQ, incur an increasing communication overhead, whereas low-communication alternatives, such as JSQ(d) and the recently proposed JIQ scheme are either unstable or provide poor performance.

We argue that a better low-communication load balancing scheme can be established by allowing each dispatcher to have a different view of the system and keep using JSQ, rather than greedily trying to avoid starvation on a per-decision basis.
accordingly, we introduce the Loosely-Shortest -Queue family of load balancing algorithms. Roughly speaking, in Loosely-shortest -Queue, each dispatcher keeps a different approximation of the server queue lengths and routes jobs to the shortest among them. Communication is used only to update the approximations and make sure that they are not too far from the real queue lengths in expectation. We formally establish the strong stability of any Loosely-Shortest -Queue policy and provide an easy-to-verify sufficient condition for verifying that a policy is Loosely-Shortest -Queue. We further demonstrate that the Loosely-Shortest -Queue approach allows constructing throughput optimal policies with an arbitrarily low communication budget.

Finally, using extensive simulations that consider homogeneous, heterogeneous and highly skewed heterogeneous systems in scenarios with a single dispatcher as well as with multiple dispatchers, we show that the examined Loosely-Shortest -Queue example policies are always stable as dictated by theory. Moreover, it exhibits an appealing performance and significantly outperforms well-known low-communication policies, such as JSQ(d) and JIQ, while using a similar communication budget.
Answer:
Scalable and low communication load balancing solution for heterogeneous-server multi-dispatcher systems with strong theoretical guarantees and promising empirical results.