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In the paper we investigate a practical approach to application of integer linear programming for optimization of data assignment to compute units in a multi-level heterogeneous environment with various compute devices, including CPUs, GPUs and Intel Xeon Phis. The model considers an application that processes a large number of data chunks in parallel on various compute units and takes into account computations, communication including bandwidths and latencies, partitioning, merging, initialization, overhead for computational kernel launch and cleanup. We show that theoretical results from our model are close to real results as differences do not exceed 5% for larger data sizes, with up to 16.7% for smaller data sizes. For an exemplary workload based on solving systems of equations of various sizes with various compute-to-communication ratios we demonstrate that using an integer linear programming solver (lp_solve) with timeouts allows to obtain significantly better total (solver+application) run times than runs without timeouts, also significantly better than arbitrary chosen ones. We show that OpenCL 1.2’s device fission allows to obtain better performance in heterogeneous CPU+GPU environments compared to the GPU-only and the default CPU+GPU configuration, where a whole device is assigned for computations leaving no resources for GPU management.

Abstract-Mappings verification is a laborious task. The paper presents a Game with a Purpose based system for verification of automatically generated mappings. General description of idea standing behind the games with the purpose is given. Description of TGame system, a 2D platform mobile game with verification process included in the gameplay, is provided. Additional mechanisms for anti-cheating, increasing player's motivation and gathering feedback are also presented. Example of the system usage for verification of mappings between WordNet synsets and Wikipedia articles is presented. The evaluation of proposed solution and future work is also described.

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The paper presents an approach to build references (also called mappings) between WordNet and Wikipedia. We propose four algorithms used for automatic construction of the references. Then, based on an aggregation algorithm, we produce an initial set of mappings that has been evaluated in a cooperative way. For that purpose, we implement a system for the distribution of evaluation tasks, that have been solved by the user community. To make the tasks more attractive, we embed them into a game. Results show the initial mappings have good quality, and they have also been improved by the community. As a result, we deliver a high quality dataset of the mappings between two lexical repositories: WordNet and Wikipedia, that can be used in a wide range of NLP tasks. We also show that the framework for collaborative validation can be used in other tasks that require human judgments.