Pushing Big Data into Accelerators

Abstract

Advancements in the field of big data have led into an increasing interest in accelerator-based computing as a solution for computationally intensive problems. However, many prevalent big data frameworks are built and run on top of the Java Virtual Machine (JVM), which does not explicitly offer support for accelerated computing with e.g. GPGPU or FPGA. One major challenge in combining JVM-based big data frameworks with accelerators is transferring data from objects that reside in JVM managed memory to the accelerator. In this paper, a rigorous analysis of possible solutions is presented to address this challenge. Furthermore, a tool is presented which generates the required code for four alternative solutions and measures the attainable data transfer speed, given a specific object graph. This can give researchers and designers a fast insight about whether the interface between JVM and accelerator can saturate the computational resources of their accelerator. The benchmarking tool was run on a POWER8 system, for which results show that depending on the size of the objects and collections size, an approach based on the Java Native Interface can achieve between 0.9 and 12 GB/s, ByteBuffers can achieve between 0.7 and 3.3 GB/s, the Unsafe library can achieve between 0.8 and 16 GB/s and finally an approach access the data directly can achieve between 3 and 67 GB/s. From our measurements, we conclude that the HotSpot VM does not yet have standardized interfaces by design that can saturate common bandwidths to accelerators seen today or in the future, although one of the approaches presented in this paper can overcome this limitation.