The increasingly heterogeneous modern hardware landscape is forcing database vendors to rethink basic design decisions: With more and more architectures to support, the traditional approach of building on hand-tuned operators might simply become too costand labor-intensive. With this problem in mind, we introduced the notion of a hardware-oblivious database engine, which avoids devicespecific optimizations and targets multiple different hardware architectures from a single code-base. We demonstrated the feasibility of this concept through Ocelot, a prototypical hardware-oblivious database that uses OpenCL to provide operators that can run on multiple architectures. In this demonstration, we show how we modified Ocelot to support self-learning algorithm adaptivity: The ability to automatically learn which algorithms are optimal for a given operation on a given hardware architecture. We present how to specify operators that can be executed by multiple algorithms, provide details about the underlying learning and decision routines, and demonstrate how our system picks the optimal algorithm when running on systems with multiple devices, such as CPUs and graphics cards.

The past years saw the emergence of highly heterogeneous server architectures that feature multiple accelerators in addition to the main processor. Efficiently exploiting these systems for data processing is a challenging research problem that comprises many facets, including how to find an optimal operator placement strategy, how to estimate runtime costs across different hardware architectures, and how to manage the code and maintenance blowup caused by having to support multiple architectures. In prior work, we already discussed solutions to some of these problems: First, we showed that specifying operators in a hardware-oblivious way can prevent code blowup while still maintaining competitive performance when supporting multiple architectures. Second, we presented learning cost functions and several heuristics to efficiently place operators across all available devices. In this demonstration, we provide further insights into this line of work by presenting our combined system Ocelot/HyPE. Our system integrates a hardware-oblivious data processing engine with a learning query optimizer for placement decisions, resulting in a highly adaptive DBMS that is specifically tailored towards heterogeneous hardware environments.

The multi-core architectures of today's computer systems make parallelism a necessity for performance critical applications. Writing such applications in a generic, hardware-oblivious manner is a challenging problem: Current database systems thus rely on labor-intensive and error-prone manual tuning to exploit the full potential of modern parallel hardware architectures like multi-core CPUs and graphics cards. We propose an alternative design for a parallel database engine, based on a single set of hardware-oblivious operators, which are compiled down to the actual hardware at runtime. This design reduces the development overhead for parallel database engines, while achieving competitive performance to hand-tuned systems. We provide a proof-of-concept for this design by integrating operators written using the parallel programming framework OpenCL into the open-source database MonetDB. Following this approach, we achieve efficient, yet highly portable parallel code without the need for optimization by hand. We evaluated our implementation against MonetDB using TPC-H derived queries and observed a performance that rivals that of MonetDB's query execution on the CPU and surpasses it on the GPU. In addition, we show that the same set of operators runs nearly unchanged on a GPU, demonstrating the feasibility of our approach.