Speculative parallelization aggressively executes in parallel codes that cannot be fully parallelized by the compiler. Past proposals of hardware schemes have mostly focused on single-chip multiprocessors (CMPs), whose effectiveness is necessarily limited by their small size. Very few schemes have attempted this technique in the context of scalable shared-memory systems.In this paper, we present and evaluate a new hardware scheme for scalable speculative parallelization. This design needs relatively simple hardware and is efficiently integrated into a cache-coherent NUMA system. We have designed the scheme in a hierarchical manner that largely abstracts away the internals of the node. We effectively utilize a speculative CMP as the building block for our scheme.Simulations show that the architecture proposed delivers good speedups at a modest hardware cost. For a set of important nonanalyzable scientific loops, we report average speedups of 4.2 for 16 processors. We show that support for per-word speculative state is required by our applications, or else the performance suffers greatly.
Abstract-We present the application of customized code generation to database query evaluation. The idea is to use a collection of highly efficient code templates and dynamically instantiate them to create query-and hardware-specific source code. The source code is compiled and dynamically linked to the database server for processing. Code generation diminishes the bloat of higher-level programming abstractions necessary for implementing generic, interpreted, SQL query engines. At the same time, the generated code is customized for the hardware it will run on. We term this approach holistic query evaluation. We present the design and development of a prototype system called HIQUE, the Holistic Integrated Query Engine, which incorporates our proposals. We undertake a detailed experimental study of the system's performance. The results show that HIQUE satisfies its design objectives, while its efficiency surpasses that of both wellestablished and currently-emerging query processing techniques.
Non-volatile memory (NVM) is emerging as a fast byte-addressable alternative for storing persistent data. Ensuring atomic durability in NVM requires logging. Existing techniques have proposed software logging either by using streaming stores for an undo log; or, by relying on the combination of clflush and mfence for a redo log. These techniques are suboptimal because they waste precious execution cycles to implement logging, which is fundamentally a data movement operation. We propose ATOM, a hardware log manager based on undo logging that performs the logging operation out of the critical path. We present the design principles behind ATOM and two techniques to optimize its performance. Our results show that ATOM achieves an improvement of 27% to 33% for micro-benchmarks and 60% for TPC-C over a baseline undo log design.
With speculative thread-level parallelization, codes that cannot be fully compiler-analyzed are aggressively executed in parallel. If the hardware detects a cross-thread dependence violation, it squashes offending threads and resumes execution. Unfortunately, frequent squashing cripples performance. This paper proposes a new framework of hardware mechanisms to eliminate most squashes due to data dependences in multiprocessors. The framework works by learning and predicting violations, and applying delayed disambiguation, value prediction, and stall and release. The framework is suited for directory-based multiprocessors that track memory accesses at the system level with the coarse granularity of memory lines. Simulations of a 16-processor machine show that the framework is very effective. By adding our framework to a speculative CC-NUMA with 64-byte memory lines, we speed-up applications by an average of 4.3 times. Moreover, the resulting system is even 23% faster than a machine that tracks memory accesses at the fine granularity of words -a sophisticated system that is not compatible with mainstream cache coherence protocols.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.