As a general abstraction for coordinating the distributed storage and access of model parameters, the parameter server (PS) architecture enables distributed machine learning to handle large datasets and high dimensional models. Many systems, such as Parameter Server and Petuum, have been developed based on the PS architecture and widely used in practice. However, none of these systems supports changing parallelism during runtime, which is crucial for the efficient execution of machine learning tasks with dynamic workloads. We propose a new system, called FlexPS, which introduces a novel multi-stage abstraction to support flexible parallelism control. With the multi-stage abstraction, a machine learning task can be mapped to a series of stages and the parallelism for a stage can be set according to its workload. Optimizations such as stage scheduler, stage-aware consistency controller, and direct model transfer are proposed for the efficiency of multi-stage machine learning in FlexPS. As a general and complete PS systems, FlexPS also incorporates many optimizations that are not limited to multi-stage machine learning. We conduct extensive experiments using a variety of machine learning workloads, showing that FlexPS achieves significant speedups and resource saving compared with the state-of-the-art PS systems such as Petuum and Multiverso.

Locality Sensitive Hashing (LSH) algorithms are widely adopted to index similar items in high dimensional space for approximate nearest neighbor search. As the volume of real-world datasets keeps growing, it has become necessary to develop distributed LSH solutions. Implementing a distributed LSH algorithm from scratch requires high development costs, thus most existing solutions are developed on general-purpose platforms such as Hadoop and Spark. However, we argue that these platforms are both hard to use for programming LSH algorithms and inefficient for LSH computation. We propose LoSHa, a distributed computing framework that reduces the development cost by designing a tailor-made, general programming interface and achieves high efficiency by exploring LSH-specific system implementation and optimizations. We show that many LSH algorithms can be easily expressed in LoSHa's API. We evaluate LoSHa and also compare with general-purpose platforms on the same LSH algorithms. Our results show that LoSHa's performance can be an order of magnitude faster, while the implementations on LoSHa are even more intuitive and require few lines of code.

Coarse-grained operators such as map and reduce have been widely used for large-scale data processing. While they are easy to master, over-simplified APIs sometimes hinder programmers from fine-grained control on how computation is performed and hence designing more efficient algorithms. On the other hand, resorting to domain-specific languages (DSLs) is also not a practical solution, since programmers may need to learn how to use many systems that can be very different from each other, and the use of low-level tools may even result in bug-prone programming.

Tensors are higher order generalizations of matrices to model multi-aspect data, e.g., a set of purchase records with the schema (user_id, product_id, timestamp, feedback). Tensor factorization is a powerful technique for generating a model from a tensor, just like matrix factorization generates a model from a matrix, but with higher accuracy and richer information as more attributes are available in a higher- order tensor than a matrix. The data model obtained by tensor factorization can be used for classification, recommendation, anomaly detection, and so on. Though having a broad range of applications, tensor factorization has not been popularly applied compared with matrix factorization that has been widely used in recommender systems, mainly due to the high computational cost and poor scalability of existing tensor factorization methods. Efficient and scalable tensor factorization is particularly challenging because real world tensor data are mostly sparse and massive. In this paper, we propose a novel distributed algorithm, called Lock-Free Tensor Factorization (LFTF), which significantly improves the efficiency and scalability of distributed tensor factorization by exploiting asynchronous execution in a re-formulated problem. Our experiments show that LFTF achieves much higher CPU and network throughput than existing methods, converges at least 17 times faster and scales to much larger datasets.

General-purpose distributed systems for data processing become popular in recent years due to the high demand from industry for big data analytics. However, there is a lack of comprehensive comparison among these systems and detailed analysis on their performance. In this paper, we conduct an extensive performance study on four state-of-the-art general-purpose distributed computing systems. Our results reveal useful insights on the design and implementation, which help the improvement of existing systems and the development of better new systems.

Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems employ a user-friendly "think like a vertex" programming model, and exhibit good scalability for tasks where the majority of graph vertices participate in computation. However, the design of these systems can seriously under-utilize the resources in a cluster for processing light-workload graph queries, where only a small fraction of vertices need to be accessed. In this work, we develop a new open-source system, called Quegel , for querying big graphs. Quegel treats queries as first-class citizens in its design: users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand, using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.

Finding efficient, expressive and yet intuitive programming models for data-parallel computing system is an important and open problem. Systems like Hadoop and Spark have been widely adopted for massive data processing, as coarse-grained primitives like map and reduce are succinct and easy to master. However, sometimes over-simplified API hinders programmers from more fine-grained control and designing more efficient algorithms. Developers may have to resort to sophisticated domain-specific languages (DSLs), or even low-level layers like MPI, but this raises development cost---learning many mutually exclusive systems prolongs the development schedule, and the use of low-level tools may result in bugprone programming. This motivated us to start the Husky open-source project, which is an attempt to strike a better balance between high performance and low development cost. Husky is developed mainly for in-memory large scale data mining, and also serves as a general research platform for designing efficient distributed algorithms. We show that many existing frameworks can be easily implemented and bridged together inside Husky, and Husky is able to achieve similar or even better performance compared with domain-specific systems.