Matrix multiplication is a dominant but very time-consuming operation in many big data analytic applications. Thus its performance optimization is an important and fundamental research issue. The performance of large-scale matrix multiplication on distributed data-parallel platforms is determined by both computation and IO costs. For existing matrix multiplication execution strategies, when the execution concurrency scales up above a threshold, their execution performance deteriorates quickly because the increase of the IO cost outweighs the decrease of the computation cost. This paper presents a novel parallel execution strategy CRMM (Concurrent Replication-based Matrix Multiplication) along with a parallel algorithm, Marlin, for large-scale matrix multiplication on data-parallel platforms. The CRMM strategy exploits higher execution concurrency for sub-block matrix multiplication with the same IO cost. To further improve the performance of Marlin, we also propose a number of novel system-level optimizations, including increasing the concurrency of local data exchange by calling native library in batch, reducing the overhead of block matrix transformation, and reducing disk heavy shuffle operations by exploiting the semantics of matrix computation. We have implemented Marlin as a library along with a set of related matrix operations on Spark and also contributed Marlin to the open-source community. For large-sized matrix multiplication, Marlin outperforms existing systems including Spark MLlib, SystemML and SciDB, with about 1.29×, 3.53× and 2.21× speedup on average, respectively. The evaluation upon a real-world DNN workload also indicates that Marlin outperforms above systems by about 12.8×, 5.1× and 27.2× speedup, respectively.

As location-based services (LBSs) become popular, location-dependent queries have raised serious privacy concerns since they may disclose sensitive information in query processing. Among typical queries supported by LBSs, shortest path queries may reveal information about not only current locations of the clients, but also their potential destinations and travel plans. Unfortunately, existing methods for private shortest path computation suffer from issues of weak privacy property, low performance or poor scalability. In this paper, we aim at a strong privacy guarantee, where the adversary cannot infer almost any information about the queries, with better performance and scalability. To achieve this goal, we introduce a general system model based on the concept of Oblivious Storage (OS), which can deal with queries requiring strong privacy properties. Furthermore, we propose a new oblivious shuffle algorithm to optimize an existing OS scheme. By making trade-offs between query performance, scalability and privacy properties, we design different schemes for private shortest path computation. Eventually, we comprehensively evaluate our schemes upon real road networks in a practical environment and show their efficiency.