Designing an Adaptive VM That Combines Vectorized and JIT Execution on Heterogeneous Hardware

Abstract

Modern hardware tends to become increasingly heterogeneous which leads to major challenges for existing systems: (a) Given that performance on modern hardware should be maximized, hardware features should be fully exploited. Together with the increasing heterogeneity this leads to more complex systems. (b) In general it is non-trivial for a system to determine the most efficient way to execute a program on a specific piece of hardware. Based on (a) and (b) we believe it is necessary to extend code generation in data processing systems. First, to mitigate the increasing complexity we propose the usage of domain-specific languages (DSL) that abstract specific details away. Our DSL is based on data-parallel operations and control-flow statements which allows to easily exploit SIMD (on multiple architectures: CPU, GPU etc.). We briefly sketch our idea of such a DSL. Second, we propose a virtual machine executing this DSL. We plan to exploit different implementation flavors (adaptivity) and dynamically compile & optimize hot paths in the program (JITcompilation). We sketch ideas about which paths to compile, how to achieve adaptive execution of different JIT-compiled paths and elaborate a bit more on our ideas on workload-specific optimizations. Finally, we present our plan for future research and ideas for major publications.

Charting the design space of query execution using VOILA

“…During query execution, the VM can start interpreting code fragments using highly efficient vectorized interpretation. Later, the VM can generate optimized code for expensive fragments, tailored specifically to the current workload and hardware [17].…”

Fluid Co-processing

“…As for the Fluid Co-Processing framework, we intend to mature our prototype into a system that can execute generic and concurrent queries and perform adaptive offloading to heterogeneous hardware [11].…”