A case for effective utilization of Direct Cache Access for big data workloads

The exploration of techniques to accelerate big data applicationshas been an active area of research. Although we have highly efficient computing cores and high-speed networks, the bottleneck in most big data applications has been the latency of data access. The foremost contributors to this latency are the network communication, storage systems, software stack and data transfer. Heterogeneous co-processors, FPGA accelerators, and flash based storage accelerators try to overcome this latency by offloading processing from the primary processor, but these cause additional overheads to an already costly data-center server and increase the total deployment cost. With an ever growing size of data, the need to exploit the available resources in the primary processor while achieving the best possible performance becomes increasingly necessary. A humble performance improvement of even 1\% goes a long way in a typical data center environment. Consequently, this work evaluates the effectiveness of Data Direct Input Output (DDIO) commonly known as Direct Cache Access (DCA) for I/O intensive big data workloads. We begin with a survey of various kinds and characteristics of big data workloads and then present the performance gain/loss due to DCA for I/O intensive workloads on Xeon E5 based servers. The big data applications are considerably different from the workloads traditionally used in architectural studies hence micro-benchmarks are used to emulate workloads which could gain/lose considerable performance when using direct cache access. Also, we present the performance of I/O intensive tasks from state of the art Cloudsuite benchmark suite. We finally make a case for the dynamic use of DCA in the processor for better performance of big data applications (change the percentage of cache available for DDIO to use or the cache levels DCA can access)