DescriptionThe 10th workshop on Many-Task Computing on Clouds, Grids, and Supercomputers (MTAGS17) will provide both the scientific and industrial communities a dedicated forum for presenting new research, development, and deployment efforts of algorithms, frameworks, and systems for many-task computing (MTC), machine learning and big data applications on large scale clusters, clouds, grids, and supercomputers. The theme of the workshop encompasses loosely-coupled applications driven by big data. The applications are generally composed of many tasks (e.g., millions to billions) to achieve some larger application goal. This workshop will cover challenges that can hamper efficiency and utilization in running applications on extreme-scale systems, such as local resource manager’s scalability and granularity, data-aware scheduling, efficient utilization of intra-node parallelism, parallel file-system contention and scalability, data locality, I/O management, reliability at scale, and application scalability. We welcome paper submissions in theoretical, simulations, and real systems topics with special consideration to papers addressing the intersection of petascale/exascale challenges with large-scale cloud computing and machine learning. Papers will be peer-reviewed, and accepted papers will be published in the workshop proceedings as part of the ACM SIGHPC. The workshop will be held in conjunction with SC17---The International Conference on High Performance Computing, Networking, Storage and Analysis---in Denver, Colorado, USA.