The performance of policy gradient methods is sensitive to hyperparameter settings that must be tuned for any new application. Widely used grid search methods for tuning hyperparameters are sample inefficient and computationally expensive. More advanced methods like Population Based Training that learn optimal schedules for hyperparameters instead of fixed settings can yield better results, but are also sample inefficient and computationally expensive. In this paper, we propose Hyperparameter Optimisation on the Fly (HOOF), a gradient-free meta-learning algorithm that can automatically learn an optimal schedule for hyperparameters that affect the policy update directly through the gradient. The main idea is to use existing trajectories sampled by the policy gradient method to optimise a one-step improvement objective, yielding a sample and computationally efficient algorithm that is easy to implement. Our experimental results across multiple domains and algorithms show that using HOOF to learn these hyperparameter schedules leads to faster learning with improved performance.
By contrast, parallel search methods like grid search and random search run multiple training runs with different hyperparameter settings in parallel to reduce wall clock time, but require more parallel computational resources. These methods are easy to implement, and have been shown to perform well @cite_0 @cite_23 .
@cite_0 @cite_23
2106411961 2097998348
Extract-Transform-Load (ETL) programs are used to load data into data warehouses (DWs). An ETL program must extract data from sources, apply different transformations to it, and use the DW to look up insert the data. It is both time consuming to develop and to run an ETL program. It is, however, typically the case that the ETL program can exploit both task parallelism and data parallelism to run faster. This, on the other hand, makes the development time longer as it is complex to create a parallel ETL program. To remedy this situation, we propose efficient ways to parallelize typical ETL tasks and we implement these new constructs in an ETL framework. The constructs are easy to apply and do only require few modifications to an ETL program to parallelize it. They support both task and data parallelism and give the programmer different possibilities to choose from. An experimental evaluation shows that by using a little more CPU time, the (wall-clock) time to run an ETL program can be greatly reduced. This paper argues that computational grids can be used for far more types of applications than just trivially parallel ones. Algorithmic optimizations like latency-hiding and exploiting locality can be used effectively to obtain high performance on grids, despite the relatively slow wide-area networks that connect the grid resources. Moreover, the bandwidth of wide-area networks increases rapidly, allowing even some applications that are extremely communication intensive to run on a grid, provided the underlying algorithms are latency-tolerant. We illustrate large-scale parallel computing on grids with three example applications that search large state spaces: transposition-driven search, retrograde analysis, and model checking. We present several performance results on a state-of-the-art computer science grid (DAS-3) with a dedicated optical network. Web search engines are optimized to reduce the high-percentile response time to consistently provide fast responses to almost all user queries. This is a challenging task because the query workload exhibits large variability, consisting of many short-running queries and a few long-running queries that significantly impact the high-percentile response time. With modern multicore servers, parallelizing the processing of an individual query is a promising solution to reduce query execution time, but it gives limited benefits compared to sequential execution since most queries see little or no speedup when parallelized. The root of this problem is that short-running queries, which dominate the workload, do not benefit from parallelization. They incur a large parallelization overhead, taking scarce resources from long-running queries. On the other hand, parallelization substantially reduces the execution time of long-running queries with low overhead and high parallelization efficiency. Motivated by these observations, we propose a predictive parallelization framework with two parts: (1) predicting long-running queries, and (2) selectively parallelizing them. For the first part, prediction should be accurate and efficient. For accuracy, we study a comprehensive feature set covering both term features (reflecting dynamic pruning efficiency) and query features (reflecting query complexity). For efficiency, to keep overhead low, we avoid expensive features that have excessive requirements such as large memory footprints. For the second part, we use the predicted query execution time to parallelize long-running queries and process short-running queries sequentially. We implement and evaluate the predictive parallelization framework in Microsoft Bing search. Our measurements show that under moderate to heavy load, the predictive strategy reduces the 99th-percentile response time by 50 (from 200 ms to 100 ms) compared with prior approaches that parallelize all queries. When we write or prepare to write a research paper, we always have appropriate references in mind. However, there are most likely references we have missed and should have been read and cited. As such a good citation recommendation system would not only improve our paper but, overall, the efficiency and quality of literature search. Usually, a citation's context contains explicit words explaining the citation. Using this, we propose a method that "translates" research papers into references. By considering the citations and their contexts from existing papers as parallel data written in two different "languages", we adopt the translation model to create a relationship between these two "vocabularies". Experiments on both CiteSeer and CiteULike dataset show that our approach outperforms other baseline methods and increase the precision, recall and f-measure by at least 5 to 10 , respectively. In addition, our approach runs much faster in the both training and recommending stage, which proves the effectiveness and the scalability of our work.