Machine learning (ML) is at the forefront of the rising popularity of data-driven software applications. The resulting rapid proliferation of ML technology, explosive data growth, and shortage of data science expertise have caused the industry to face increasingly challenging demands to keep up with fast-paced develop-and-deploy model lifecycles. Recent academic and industrial research efforts have started to address this problem through automated machine learning (AutoML) pipelines and have focused on model performance as the first-order design objective. We present Oracle AutoML, a novel iteration-free AutoML pipeline designed to not only provide accurate models, but also in a shorter runtime. We are able to achieve these objectives by eliminating the need to continuously iterate over various pipeline configurations. In our feed-forward approach, each pipeline stage makes decisions based on metalearned proxy models that can predict candidate pipeline configuration performances before building the full final model. Our approach, which builds and tunes only the best candidate pipeline, achieves better scores at a fraction of the time compared to state-of-the-art open source AutoML tools, such as H2O and Auto-sklearn. This makes Oracle AutoML a prime candidate for addressing current industry challenges.

Today, an ever increasing amount of transistors are packed into processor designs with extra features to support a broad range of applications. As a consequence, processors are becoming more and more complex and power hungry. At the same time, they only sustain an average performance for a wide variety of applications while not providing the best performance for specific applications. In this paper, we demonstrate through a carefully designed modern data processing system called RAPID and a simple, low-power processor specially tailored for data processing that at least an order of magnitude performance/power improvement in SQL processing can be achieved over a modern system running on today's complex processors. RAPID is designed from the ground up with hardware/software co-design in mind to provide architecture-conscious extreme performance while consuming less power in comparison to the modern database systems. The paper presents in detail the design and implementation of RAPID, a relational, columnar, in-memory query processing engine supporting analytical query workloads.

For many years, the highest energy cost in processing has been data movement rather than computation, and energy is the limiting factor in processor design [21]. As the data needed for a single application grows to exabytes [56], there is clearly an opportunity to design a bandwidth-optimized architecture for big data computation by specializing hardware for data movement. We present the Data Processing Unit or DPU, a shared memory many-core that is specifically designed for high bandwidth analytics workloads. The DPU contains a unique Data Movement System (DMS), which provides hardware acceleration for data movement and partitioning operations at the memory controller that is sufficient to keep up with DDR bandwidth. The DPU also provides acceleration for core to core communication via a unique hardware RPC mechanism called the Atomic Transaction Engine. Comparison of a DPU chip fabricated in 40nm with a Xeon processor on a variety of data processing applications shows a 3× - 15× performance per watt advantage.

Storage-class memory technologies such as phase-change memory and memristors present a radically different interface to storage than existing block devices. As a result, they provide a unique opportunity to re-examine storage architectures. We find that the existing kernel-based stack of components, well suited for disks, unnecessarily limits the design and implementation of file systems for this new technology.

Cloud computing promises great efficiencies by multiplexing resources among disparate customers. For example, Amazon's Elastic Compute Cloud (EC2), Microsoft Azure, Google's Compute Engine, and Rack-space Hosting all offer Infrastructure as a Service (IaaS) solutions that pack multiple customer virtual machines (VMs) onto the same physical server.

Infrastructure-as-a-system compute clouds such as Amazon's EC2 allow users to pay a flat hourly rate to run their virtual machine (VM) on a server providing some combination of CPU access, storage, and network. But not all VM instances are created equal: distinct underlying hardware differences, contention, and other phenomena can result in vastly differing performance across supposedly equivalent instances. The result is striking variability in the resources received for the same price.