Benefits of base64 encoding1/8/2024 These results move our modified RPFC into a practical range for use in database systems. Our experimental evaluation shows that our proposed techniques accelerate compression and access times by up to 24x and 2.9x, respectively. Moreover, to accelerate access times, we devise a vectorized access method, using Intel® Advanced Vector Extensions 512 (Intel® AVX-512), that is enabled by two specific changes we propose to RPFC. To accelerate compression times, we propose block-based RPFC, which consists in compressing independently small blocks of the dictionary. We focus on Re-Pair Front Coding (RPFC), a grammar-based compression algorithm, since it consistently offers better compression ratios than other algorithms in the literature. This paper endeavors to make strong string dictionary compression practical. Therefore, lightweight algorithms such as front coding are favored in practice. While strong string dictionary compression algorithms exist, these come with impractical access and compression times. String dictionaries constitute a large portion of the memory foot-print of database applications. © 2018 Springer Science+Business Media, LLC, part of Springer Nature Moreover, these successful performance rates make SWIMM 2.0 the most efficient energy footprint implementation in this study achieving 2.94 GCUPS/Watts on the SKL processor. It is competitive in terms of performance compared with other state-of-the-art implementations, reaching 511 GCUPS on a single KNL node and 734 GCUPS on a server equipped with a dual SKL processor. SWIMM 2.0 is based on a massive multi-threading and SIMD exploitation. The novelty of this vector instruction set requires the revision of previous programming and optimization techniques. In this paper, we present an SW version that is optimized for both architectures: the renowned SWIMM 2.0. This SIMD set is currently supported by Intel’s Knights Landing (KNL) accelerator and Intel’s Skylake (SKL) general purpose processors. Although the acceleration of SW has already been studied on many parallel platforms, there are hardly any studies which take advantage of the latest Intel architectures based on AVX-512 vector extensions. The well-known Smith–Waterman (SW) algorithm is the most commonly used method for local sequence alignments, but its acceptance is limited by the computational requirements for large protein databases. Energy improvements over scalar codes in a single-thread environment range from 2\(\times \) for Streamcluster (worst) to 8\(\times \) for Blackscholes (best). Results show that the performance and energy improvements depend greatly on the fraction of code that can be vectorized We use Intel’s Top–Down model to show the main bottlenecks of the architecture for each studied benchmark. ParVec is extended to add support for AVX-512 as well as the newest versions of the GCC compiler In this paper, we perform a scalability and energy efficiency analysis of AVX-512 using the ParVec benchmark suite. Intel’s AVX-512 (512-bit registers) and ARM’s SVE (up to 2048-bit registers) are examples of such trend. Companies are pushing the size of vector registers to double every 4 years. single-instruction multiple-data execution). While instruction-level and thread-level parallelism are embraced by developers, data-level parallelism is usually underutilized, despite its huge potential (e.g. Parallelism is the basic foundation for achieving the exascale level. Optimizing the usage of the available transistors within the TDP is a pending topic. Energy efficiency below a specific thermal design power (TDP) has become the main design goal for microprocessors across all market segments.
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