Analytical Query Processing Using Heterogeneous SIMD Instruction Sets

Numerous applications gather increasing amounts of data, which have to be managed and queried. Different hardware developments help to meet this challenge. The grow-ing capacity of main memory enables database systems to keep all their data in memory. Additionally, the hardware landscape is becoming more diverse. A plethora of homo-geneous and heterogeneous co-processors is available, where heterogeneity refers not only to a different computing power, but also to different instruction set architectures. For instance, modern Intel® CPUs offer different instruction sets supporting the Single Instruction Multiple Data (SIMD) paradigm, e.g. SSE, AVX, and AVX512. Database systems have started to exploit SIMD to increase performance. However, this is still a challenging task, because existing algorithms were mainly developed for scalar processing and because there is a huge variety of different instruction sets, which were never standardized and have no unified interface. This requires to completely rewrite the source code for porting a system to another hardware architecture, even if those archi-tectures are not fundamentally different and designed by the same company. Moreover, operations on large registers, which are the core principle of SIMD processing, behave counter-intuitively in several cases. This is especially true for analytical query process-ing, where different memory access patterns and data dependencies caused by the com-pression of data, challenge the limits of the SIMD principle. Finally, there are physical constraints to the use of such instructions affecting the CPU frequency scaling, which is further influenced by the use of multiple cores. This is because the supply power of a CPU is limited, such that not all transistors can be powered at the same time. Hence, there is a complex relationship between performance and power, and therefore also between performance and energy consumption. This thesis addresses the specific challenges, which are introduced by the application of SIMD in general, and the heterogeneity of SIMD ISAs in particular. Hence, the goal of this thesis is to exploit the potential of heterogeneous SIMD ISAs for increasing the performance as well as the energy-efficiency

Make Larger Vector Register Sizes New Challenges?: Lessons Learned from the Area of Vectorized Lightweight Compression Algorithms

The exploitation of data as well as hardware properties is a core aspect for efficient data management. This holds in particular for the field of in-memory data processing. Aside from increasing main memory capacities, in-memory data processing also benefits from novel processing concepts based on lightweight compressed data. To speed up compression as well as decompression, an active research field deals with the specialization of these algorithms to hardware features such as vectorization using SIMD instructions. Most of the vectorized implementations have been proposed for 128 bit vector registers. However, hardware vendors still increase the vector register sizes, whereby a straightforward transformation to these wider vector sizes is possible in most-cases. Thus, we systematically investigated the impact of different SIMD instruction set extensions with wider vector sizes on the behavior of straightforward transformed implementations. In this paper, we will describe our evaluation methodology and present selective results of our exhaustive evaluation. In particular, we will highlight some challenges and present first approaches to tackle them

Energy Elasticity on Heterogeneous Hardware using Adaptive Resource Reconfiguration LIVE

Energy awareness of database systems has emerged as a critical research topic, since energy consumption is becoming a major limiter for their scalability. Recent energy-related hardware developments trend towards offering more and more configuration opportunities for the software to control its own energy consumption. Existing research so far mainly focused on leveraging this configuration spectrum to find the most energy-efficient configuration for specific operators or entire queries. In this demo, we introduce the concept of energy elasticity and propose the energy-control loop as an implementation of this concept. Energy elasticity refers to the ability of software to behave energy-proportional and energy-efficient at the same time while maintaining a certain quality of service. Thus, our system does not draw the least energy possible but the least energy necessary to still perform reasonably. We demonstrate our overall approach using a rich interactive GUI to give attendees the opportunity to learn more about our concept

From a Comprehensive Experimental Survey to a Cost-based Selection Strategy for Lightweight Integer Compression Algorithms

Lightweight integer compression algorithms are frequently applied in in-memory database systems to tackle the growing gap between processor speed and main memory bandwidth. In recent years, the vectorization of basic techniques such as delta coding and null suppression has considerably enlarged the corpus of available algorithms. As a result, today there is a large number of algorithms to choose from, while different algorithms are tailored to different data characteristics. However, a comparative evaluation of these algorithms with different data and hardware characteristics has never been sufficiently conducted in the literature. To close this gap, we conducted an exhaustive experimental survey by evaluating several state-of-the-art lightweight integer compression algorithms as well as cascades of basic techniques. We systematically investigated the influence of data as well as hardware properties on the performance and the compression rates. The evaluated algorithms are based on publicly available implementations as well as our own vectorized reimplementations. We summarize our experimental findings leading to several new insights and to the conclusion that there is no single-best algorithm. Moreover, in this article, we also introduce and evaluate a novel cost model for the selection of a suitable lightweight integer compression algorithm for a given dataset

Balancing Performance and Energy for Lightweight Data Compression Algorithms

Energy consumption becomes more and more a critical design factor, whereby performance is still an important requirement. Thus, a balance between performance and energy has to be established. To tackle that issue for database systems, we proposed the concept of work-energy profiles. However, generating such profiles requires extensive benchmarking. To overcome that, we propose to approximate work-energy-profiles for complex operations based on the profiles of low-level operations in this paper. To show the feasibility of our approach, we use lightweight data compression algorithms as complex operations, since compression as well as decompression are heavily used in in-memory database systems, where data is always managed in a compressed representation. Furthermore, we evaluate our approach on a concrete hardware system

Development of a highly productive GMAW hot wire process using a two-dimensional arc deflection

Gas metal arc welding (GMAW) processes are used in a wide range of applications due to their high productivity and flexibility. Nevertheless, the supplied melting wire electrode leads to a coupling of material and heat input. Therefore, an increase of the melting rate correlates with an increase of the heat input by the arc at the same time. A possibility to separate material and heat input is to use an additional wire, which reduces penetration and worsens the wetting behaviour. Consequently, bead irregularities such as bonding defects or insufficient root weldings can occur. In the context of this article, a controlling system for a two-dimensional magnetic arc deflection is presented, which allows to influence arc position as well as material transfer. The analysed GMAW hot wire process is characterised by high melting rates while also realising a sufficient penetration depth and wetting behaviour

MorphStore — In-Memory Query Processing based on Morphing Compressed Intermediates LIVE

In this demo, we present MorphStore, an in-memory column store with a novel compression-aware query processing concept. Basically, compression using lightweight integer compression algorithms already plays an important role in existing in-memory column stores, but mainly for base data. The continuous handling of compression from the base data to the intermediate results during query processing has already been discussed, but not investigated in detail since the computational effort for compression as well as decompression is often assumed to exceed the benefits of a reduced transfer cost between CPU and main memory. However, this argument increasingly loses its validity as we are going to show in our demo. Generally, our novel compression-aware query processing concept is characterized by the fact that we are able to speed up the query execution by morphing compressed intermediate results from one scheme to another scheme to dynamically adapt to the changing data characteristics during query processing. Our morphing decisions are made using a cost-based approach

Query processing on low-energy many-core processors

Aside from performance, energy efficiency is an increasing challenge in database systems. To tackle both aspects in an integrated fashion, we pursue a hardware/software co-design approach. To fulfill the energy requirement from the hardware perspective, we utilize a low-energy processor design offering the possibility to us to place hundreds to millions of chips on a single board without any thermal restrictions. Furthermore, we address the performance requirement by the development of several database-specific instruction set extensions to customize each core, whereas each core does not have all extensions. Therefore, our hardware foundation is a low-energy processor consisting of a high number of heterogeneous cores. In this paper, we introduce our hardware setup on a system level and present several challenges for query processing. Based on these challenges, we describe two implementation concepts and a comparison between these concepts. Finally, we conclude the paper with some lessons learned and an outlook on our upcoming research directions

Decrease in expression of bone morphogenetic proteins 4 and 5 in synovial tissue of patients with osteoarthritis and rheumatoid arthritis

Bone morphogenetic proteins (BMPs) have been identified as important morphogens with pleiotropic functions in regulating the development, homeostasis and repair of various tissues. The aim of this study was to characterize the expression of BMPs in synovial tissues under normal and arthritic conditions. Synovial tissue from normal donors (ND) and from patients with osteoarthritis (OA) and rheumatoid arthritis (RA) were analyzed for BMP expression by using microarray hybridization. Differential expression of BMP-4 and BMP-5 was validated by semiquantitative RT-PCR, in situ hybridization and immunohistochemistry. Activity of arthritis was determined by routine parameters for systemic inflammation, by histological scoring of synovitis and by semiquantitative RT-PCR of IL-1β, TNF-α, stromelysin and collagenase I in synovial tissue. Expression of BMP-4 and BMP-5 mRNA was found to be significantly decreased in synovial tissue of patients with RA in comparison with ND by microarray analysis (p < 0.0083 and p < 0.0091). Validation by PCR confirmed these data in RA (p < 0.002) and also revealed a significant decrease in BMP-4 and BMP-5 expression in OA compared with ND (p < 0.015). Furthermore, histomorphological distribution of both morphogens as determined by in situ hybridization and immunohistochemistry showed a dominance in the lining layer of normal tissues, whereas chronically inflamed tissue from patients with RA revealed BMP expression mainly scattered across deeper layers. In OA, these changes were less pronounced with variable distribution of BMPs in the lining and sublining layer. BMP-4 and BMP-5 are expressed in normal synovial tissue and were found decreased in OA and RA. This may suggest a role of distinct BMPs in joint homeostasis that is disturbed in inflammatory and degenerative joint diseases. In comparison with previous reports, these data underline the complex impact of these factors on homeostasis and remodeling in joint physiology and pathology