Distributed applications for data analytics have emerged to be a significant growth domain both in terms of technology as well as business relevance. With the advent of Hadoop MapReduce, several distributed execution frameworks have been developed for data analytics and machine learning applications. Many of these frameworks are written in Java, Scala or Python and run on top of the Java Virtual Machine (JVM), leaving them vulnerable to the performance impact of garbage collection (GC) operations. Application pauses induced by the Java GC are notorious in being one of the most important contributors of runtime performance bottleneck for a single process execution. These pauses are present in the distributed scenario as well, though in this case, it is significantly more complex to measure the impact of a GC pause for the overall execution runtime. Typically, in a distributed workload, a GC can result in delayed tasks impacting the overall completion time of the application. We present herein a novel way to preemptively trigger Java GC, stealing idle cycles, so as to prevent future tasks from being impacted by a GC pause during peak execution periods, resulting in improved overall application throughput.

Atomic sections have been recently introduced as a language construct to improve the programmability of concurrent software. They simplify programming by not requiring the explicit specification of locks for shared data. Typically atomic sections are supported in software either through the use of optimistic concurrency by using transactional memory or through the use of pessimistic concurrency using compiler-assigned locks. As a software transactional memory (STM) system does not take advantage of the specific memory access patterns of an application it often suffers from false conflicts and high validation overheads. On the other hand, the compiler usually ends up assigning coarse grain locks as it relies on whole program points-to analysis which is conservative by nature. This adversely affects performance by limiting concurrency. In order to mitigate the disadvantages associated with STM's lock assignment scheme, we propose a hybrid approach which combines STM's lock assignment with a compiler aided selective lock assignment scheme (referred to as SCLA-STM). SCLA-STM overcomes the inefficiencies associated with a purely compile-time lock assignment approach by (i) using the underlying STM for shared variables where only a conservative analysis is possible by the compiler (e.g., in the presence of may-alias points to information) and (ii) being selective about the shared data chosen for the compiler-aided lock assignment. We describe our prototype SCLA-STM scheme implemented in the hp-ux IA-64 C/C++ compiler, using TL2 as our STM implementation. We show that SCLA-STM improves application performance for certain STAMP benchmarks from 1.68% to 37.13%.