It is measured on a dynamometer that test how much power and strength (torque) the engine has before the dynamometer can stop or break it. Horsepower is a measurement of an engine’s power. This gave the public an indication of how powerful his new engines were, by working out how many horses it would take to pull the same amount of weight as one of his engines. If a horse could pull a certain amount of weight per second, his engine had something for the common person to relate to. The easiest way of describing this at the time was to compare it with a horse. Initially James Watt, the inventor of the steam engine, wanted to tell people how much power his new engine could deliver. 28.Horsepower combines the theory of work with time. Navigating Big Data with High-Throughput, Energy-Efficient Data Partitioning. Navigating Big Data with High-throughput, Energy-efficient Data Partitioning. Kernel Weaver: Automatically Fusing Database Primitives for Efficient GPU Computation. Ibex - An Intelligent Storage Engine with Support for Advanced SQL Off-loading. BigDataBench: A big data benchmark suite from internet services. Database analytics acceleration using FPGAs. Flow-Join: Adaptive skew handling for distributed joins over high-speed networks. A Comparison of Approaches to Large-scale Data Analysis. Streams on Wires - A Query Compiler for FPGAs. Adaptive String Dictionary Compression in In-Memory Column-Store Database Systems. Meet the Walkers: Accelerating Index Traversals for In-memory Databases. A flexible hash table design for 10GBPS key-value stores on FPGAS. Relational Query Coprocessing on Graphics Processors. Vector Extensions for Decision Support DBMS Acceleration. QPipe: A Simultaneously Pipelined Relational Query Engine. An MPSoC for energy-efficient database query processing. Efficient Relational Database Management using Graphics Processors. Accelerating Database Operators Using a Network Processor. SharedDB: Killing One Thousand Queries with One Stone. Clearing the Clouds: A Study of Emerging Scale-out Workloads on Modern Hardware. Query Processing on Smart SSDs: Opportunities and Challenges. In Proceedings of the 5th Annual Symposium on Computer Architecture (ISCA '78). DIRECT - a Multiprocessor Organization for Supporting Relational Data Base Management Systems. Main-Memory Hash Joins on Modern Processor Architectures. A Scalable, Predictable Join Operator for Highly Concurrent Data Warehouses. In Database Machines, International Workshop. Database Machines: An Idea Whose Time Passed? A Critique of the Future of Database Machines. MonetDB/X100: Hyper-Pipelining Query Execution. Architecture of a data analytics service in hybrid cloud environments. Accelerating SQL database operations on a GPU with CUDA. An Application-specific Instruction Set for Accelerating Set-oriented Database Primitives. M7: Oracle's Next-Generation Sparc Processor. A Many-core Architecture for In-memory Data Processing. The paper presents in detail the design and implementation of RAPID, a relational, columnar, in-memory query processing engine supporting analytical query workloads. 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. 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.

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. As a consequence, processors are becoming more and more complex and power hungry. Today, an ever increasing amount of transistors are packed into processor designs with extra features to support a broad range of applications.

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