Challenges and Proposals for Enabling Dynamic Heterogeneous Execution of Big Data Frameworks

The efficient execution of Big Data applications requires a large quantity of compute and memory resources. Typically, these resources are in the form of data centres with numerous processing elements connected through a computer network. Although initially the majority of data centers were utilizing only CPU resources, nowadays we can find heterogeneous accelerators such as GPUs and FPGAs. Ideally, Big Data frameworks and applications should exploit those diverse hardware resources in order to push their performance boundaries or increase resource utilization. Despite ongoing work to enable such functionality, the majority of the solutions revolve around external libraries that provide pre-compiled kernels for heterogeneous accelerators. This fact imposes programmability and code fragmentation challenges that can only be addressed by enabling Big Data platforms to dynamically compile and execute their code on such devices. In this paper we analyze and discuss the major challenges for programming and executing Big Data processing applications on distributed systems with heterogeneous hardware. In addition, we present our work-in-progress towards providing a heterogeneous programming framework for running Big Data applications on systems that include diverse hardware resources including CPUs, GPUs, and FPGAs. In contrast to existing approaches, our envisioned solution employs JIT compilation and runtime support, integrated in the data flow engine, enabling the automatic acceleration of Big Data platforms completely transparently to the user and without sacrificing programmability.