High Productivity Computing

1. High Productivity Computing Large-scale Knowledge Discovery: Co-evolving Algorithms and Mechanisms Steve Reinhardt Principal Architect Microsoft Prof. John Gilbert, UCSB Dr. Viral Shah, UCSB

2. Context for Knowledge Discovery From Debbie Gracio and Ian Gorton, PNNL Data Intensive Computing Initiative

3. Knowledge Discovery (KD) Definition • Data-intensive computing: when the acquisition and movement of input data is a primary limitation on feasibility or performance • Simple data mining: searching for exceptional values on elemental measures (e.g., heat, #transactions) • Knowledge discovery: searching for exceptional values on associative/social measures (e.g., most between, belonging to greatest number of valuable reactions)

4. Today’s Biggest Obstacle in the KD Field • Lack of fast feedback between domain experts and infrastructure/tool developers about good usable scalable KD software platforms • Need to accelerate the rate of learning about both good KD algorithms and good KD infrastructure • Domain experts want: • Good infrastructure that works • … and scales greatly and runs fast • Flexibility to develop/tweak algorithms to suit their needs • Algorithms with strong math basis • But don’t know • The best approach or algorithms • Infrastructure developers want: • Clear audience for what they develop • Architecture that copes with client, cluster, cloud, GPU, and huge data • But don’t know • The best approach Need to get good (not perfect) scalable platforms in use to co-evolve towards best approaches and algorithms

5. Candidate Approaches

6. KDT Layers: Enable overloading with various technologies … Community Detection Elementary Mode Analysis kdt. Betweenness Centrality All Pairs Shortest Path BarycentricClustering … All Pairs Shortest Path(Cray XMT) Parallel/distributed operations (constructors, SpGEMM, SpMV, SpAdd, SpGEMM semi-rings, I/O) Parallel/distributed operations (in-memory (Star-P) or out-of-memory (DryadLINQ-based)) Localconstructors LocalSpGEMM LocalSpRef/ SpAsgn LocalSpMV LocalSpAdd LocalSpGEMMon semi-rings LocalI/O LocalSpGEMM(GPU) LocalSpGEMM(GPU) scipy.

7. DryadLINQ: Query + Plan + Parallel Execution • Dryad • Distributed-memory coarse-grain run-time • Generalized MapReduce • Using computational vertices and communication channels to form a dataflow execution graph • LINQ (Language INtegrated Query) • A query-style language interface to Dryad • Typical relational operators (e.g., Select, Join, GroupBy) • Scaling for histogram example • Input data 10.2TB, using 1,800 cluster nodes, 43,171 execution-graph verticesspawning 11,072 processes, creating 33GB output data in 11.5 minutes of execution data plane Files, TCP, FIFO, Network sched V V V NS PD PD PD control plane Job manager cluster

8. MATLAB Star-P Bridges Scientists to HPCs Star-P enables domain experts to use parallel, big-memory systems via productivity languages (e.g., the M language of MATLAB) Knowledge discovery scaling with Star-P • Kernels to 55B edges between 5B vertices, on 128 cores (consuming 4TB memory) • Compact applications to 1B edges on 256 cores

9. Next Steps • Get prototypes available for early experience and feedback • in-memory and out-of-memory targets of KDT • with graph layer • likely exposed via Python library interface

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