Interactive information retrieval services, such as enterprise search and document search, must provide relevant results with consistent, low response times in the face of rapidly growing data sets and query loads. These growing demands have led researchers to consider a wide range of optimizations to reduce response latency, including query processing parallelization and acceleration with co-processors such as GPUs. However, previous work runs queries either on GPU or CPU, ignoring the fact that the best processor for a given query depends on the query's characteristics, which may change as the processing proceeds.

Data movement between the compute and the storage (e.g., GPU and SSD) has been a long-neglected problem in heterogeneous systems, while the inefficiency in existing systems does cause significant loss in both performance and energy efficiency. This paper presents Hippogriff to provide a high-level programming model to simplify data movement between the compute and the storage, and to dynamically schedule data transfers based on system load. By eliminating unnecessary data movement, Hippogriff can speedup single program workloads by 1.17×, and save 17% energy. For multi-program workloads, Hippogriff shows 1.25× speedup. Hippogriff also improves the performance of a GPU-based MapReduce framework by 27%.

The popularity of GPUs in general purpose computation has prompted efforts to scale up MapReduce systems with GPUs, but lack of efficient I/O handling results in underutilization of shared system resources in existing systems. This paper presents SPMario, a scale-up GPU MapReduce framework to speed up job execution and boost utilization of system resources with the new I/O Oriented Scheduling. The evaluation on a set of representative benchmarks against a highly-optimized baseline system shows that for the single job cases, SPMario can speedup job execution by up to 2.28×, and boost GPU utilization by 2.12× and 2.51× for I/O utilization. When scheduling two jobs together, I/O Oriented Scheduling outperforms round-robin scheduling by up to 13.54% in total execution time, and by up to 12.27% and 14.92% in GPU and I/O utilization, respectively.

In order to take advantage of multi-core hardware, more and more applications are becoming multi-threaded. Unfortunately concurrent programs are prone to bugs, such as data races. Recently much work has been devoted to detecting data races in multi-threaded programs. Most tools, however, require the accurate knowledge of synchronizations in the program, and may otherwise suffer from false positives in race detection, limiting their usability. To address this problem, some tools such as Intel® Inspector provide mechanisms for suppressing false positives and/or annotating synchronizations not automatically recognized by the tools. However, they require users' input or even changes of the source code.