Conclave: secure multi-party computation on big data (extended TR)

Secure Multi-Party Computation (MPC) allows mutually distrusting parties to run joint computations without revealing private data. Current MPC algorithms scale poorly with data size, which makes MPC on "big data" prohibitively slow and inhibits its practical use. Many relational analytics queries can maintain MPC's end-to-end security guarantee without using cryptographic MPC techniques for all operations. Conclave is a query compiler that accelerates such queries by transforming them into a combination of data-parallel, local cleartext processing and small MPC steps. When parties trust others with specific subsets of the data, Conclave applies new hybrid MPC-cleartext protocols to run additional steps outside of MPC and improve scalability further. Our Conclave prototype generates code for cleartext processing in Python and Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave scales to data sets between three and six orders of magnitude larger than state-of-the-art MPC frameworks support on their own. Thanks to its hybrid protocols, Conclave also substantially outperforms SMCQL, the most similar existing system.Comment: Extended technical report for EuroSys 2019 pape

The End of Slow Networks: It's Time for a Redesign

Next generation high-performance RDMA-capable networks will require a fundamental rethinking of the design and architecture of modern distributed DBMSs. These systems are commonly designed and optimized under the assumption that the network is the bottleneck: the network is slow and "thin", and thus needs to be avoided as much as possible. Yet this assumption no longer holds true. With InfiniBand FDR 4x, the bandwidth available to transfer data across network is in the same ballpark as the bandwidth of one memory channel, and it increases even further with the most recent EDR standard. Moreover, with the increasing advances of RDMA, the latency improves similarly fast. In this paper, we first argue that the "old" distributed database design is not capable of taking full advantage of the network. Second, we propose architectural redesigns for OLTP, OLAP and advanced analytical frameworks to take better advantage of the improved bandwidth, latency and RDMA capabilities. Finally, for each of the workload categories, we show that remarkable performance improvements can be achieved

Massively Parallel Sort-Merge Joins in Main Memory Multi-Core Database Systems

Two emerging hardware trends will dominate the database system technology in the near future: increasing main memory capacities of several TB per server and massively parallel multi-core processing. Many algorithmic and control techniques in current database technology were devised for disk-based systems where I/O dominated the performance. In this work we take a new look at the well-known sort-merge join which, so far, has not been in the focus of research in scalable massively parallel multi-core data processing as it was deemed inferior to hash joins. We devise a suite of new massively parallel sort-merge (MPSM) join algorithms that are based on partial partition-based sorting. Contrary to classical sort-merge joins, our MPSM algorithms do not rely on a hard to parallelize final merge step to create one complete sort order. Rather they work on the independently created runs in parallel. This way our MPSM algorithms are NUMA-affine as all the sorting is carried out on local memory partitions. An extensive experimental evaluation on a modern 32-core machine with one TB of main memory proves the competitive performance of MPSM on large main memory databases with billions of objects. It scales (almost) linearly in the number of employed cores and clearly outperforms competing hash join proposals - in particular it outperforms the "cutting-edge" Vectorwise parallel query engine by a factor of four.Comment: VLDB201

Worst-Case Optimal Algorithms for Parallel Query Processing

In this paper, we study the communication complexity for the problem of computing a conjunctive query on a large database in a parallel setting with $p$ servers. In contrast to previous work, where upper and lower bounds on the communication were specified for particular structures of data (either data without skew, or data with specific types of skew), in this work we focus on worst-case analysis of the communication cost. The goal is to find worst-case optimal parallel algorithms, similar to the work of [18] for sequential algorithms. We first show that for a single round we can obtain an optimal worst-case algorithm. The optimal load for a conjunctive query $q$ when all relations have size equal to $M$ is $O(M/p^{1/\psi^*})$, where $\psi^*$ is a new query-related quantity called the edge quasi-packing number, which is different from both the edge packing number and edge cover number of the query hypergraph. For multiple rounds, we present algorithms that are optimal for several classes of queries. Finally, we show a surprising connection to the external memory model, which allows us to translate parallel algorithms to external memory algorithms. This technique allows us to recover (within a polylogarithmic factor) several recent results on the I/O complexity for computing join queries, and also obtain optimal algorithms for other classes of queries

Instance and Output Optimal Parallel Algorithms for Acyclic Joins

Massively parallel join algorithms have received much attention in recent years, while most prior work has focused on worst-optimal algorithms. However, the worst-case optimality of these join algorithms relies on hard instances having very large output sizes, which rarely appear in practice. A stronger notion of optimality is {\em output-optimal}, which requires an algorithm to be optimal within the class of all instances sharing the same input and output size. An even stronger optimality is {\em instance-optimal}, i.e., the algorithm is optimal on every single instance, but this may not always be achievable. In the traditional RAM model of computation, the classical Yannakakis algorithm is instance-optimal on any acyclic join. But in the massively parallel computation (MPC) model, the situation becomes much more complicated. We first show that for the class of r-hierarchical joins, instance-optimality can still be achieved in the MPC model. Then, we give a new MPC algorithm for an arbitrary acyclic join with load O ({\IN \over p} + {\sqrt{\IN \cdot \OUT} \over p}), where \IN,\OUT are the input and output sizes of the join, and $p$ is the number of servers in the MPC model. This improves the MPC version of the Yannakakis algorithm by an O (\sqrt{\OUT \over \IN} ) factor. Furthermore, we show that this is output-optimal when \OUT = O(p \cdot \IN), for every acyclic but non-r-hierarchical join. Finally, we give the first output-sensitive lower bound for the triangle join in the MPC model, showing that it is inherently more difficult than acyclic joins

Streamlining collection of training samples for object detection and classification in video

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