Accelerating sequential algorithms in order to achieve high performance is often a nontrivial task. However, there are certain properties that can exacerbate this process and make it particularly daunting. For example, building an efficient parallel solution for a data-intensive algorithm requires a deep analysis of the memory access patterns and data reuse potential. Attempting to scale out the computations on clusters of machines introduces further complications due to network speed limitations. In this context, the optimization landscape can be extremely complex owing to the large number of trade-off decisions. In this paper, we discuss our experience designing two parallel implementations of an existing data-intensive machine learning algorithm that detects overlapping communities in graphs. The first design uses a single GPU to accelerate the computations of small data sets. We employed a code generation strategy in order to test and identify the best performing combination of optimizations. The second design uses a cluster of machines to scale out the computations for larger problem sizes. We used a mixture of MPI, RDMA and pipelining in order to circumvent networking overhead. Both these efforts bring us closer to understanding the complex relationships hidden within networks of entities.

The impact and significance of parallel computing techniques is continuously increasing given the current trend of incorporating more cores in new processor designs. However, many Big Data systems fail to exploit the abundant computational power of multi-core CPUs and GPUs to their full potential. We present Glasswing, a scalable MapReduce framework that employs a configurable mixture of coarse- and fine-grained parallelism to achieve high performance on multi-core CPUs and GPUs. We experimentally evaluated the performance of five MapReduce applications and show that Glasswing outperforms Hadoop on a 64-node multi-core CPU cluster by a factor between 1.8 and 4, and by a factor from 20 to 30 on a 16-node GPU cluster.

Glass wing is a MapReduce framework that uses OpenCL to exploit multi-core CPUs and accelerators. However, compute device capabilities may vary significantly and require targeted optimization. Similarly, the availability of resources such as memory, storage and interconnects can severely impact overall job performance. In this paper, we present and analyze how MapReduce applications can improve their horizontal and vertical scalability by using a well controlled mixture of coarse- and fine-grained parallelism. Specifically, we discuss the Glass wing pipeline and its ability to overlap computation, communication, memory transfers and disk access. Additionally, we show how Glass wing can adapt to the distinct capabilities of a variety of compute devices by employing fine-grained parallelism. We experimentally evaluated the performance of five MapReduce applications and show that Glass wing outperforms Hadoop on a 64-node multi-core CPU cluster by factors between 1.2 and 4, and factors from 20 to 30 on a 23-node GPU cluster. Similarly, we show that Glass wing is at least 1.5 times faster than GPMR on the GPU cluster.