Large-scale data analytics is compute intensive and requires parallelization of algorithms as well as optimization of the data flow. We develop various types of parallelizations for multi-core systems and clusters. We also work with heterogeneous systems that include GPUs or vector processors.

MALT is one of our projects that enables parallelization over a large number of processors through virtual shared memory. MALT provides abstractions for fine-grained in-memory updates using one-sided RDMA, limiting data movement costs during incremental model updates. Developers can specify the dataflow while MALT takes care of communication and representation optimizations. Machine learning applications, written in C, C++ and Lua, are supported based on SVM, matrix factorization and deep learning. Besides speedup, MALT also provides fault tolerance and guarantees network efficiency. We are implementing various new distributed optimization algorithms on MALT, such as RWDDA and support for multiple GPUs.