This research focuses on evaluating and enhancing the performance of an in-house, structured, 2D CFD code - GHOST, on modern commodity clusters. The basic philosophy of this work is to optimize the cache performance of the code by splitting up the grid into smaller blocks and carrying out the required calculations on these smaller blocks. This in turn leads to enhanced code performance on commodity clusters. Accordingly, this work presents a discussion along with a detailed description of two techniques: external and internal blocking, for data access optimization. These techniques have been tested on steady, unsteady, laminar, and turbulent test cases and the results are presented. The critical hardware parameters which influenced the code performance were identified. A detailed study investigating the effect of these parameters on the code performance was conducted and the results are presented. The modified version of the code was also ported to the current state-of-art architectures with successful results.

Numerous advancements made in the field of computational sciences have made CFD a viable solution to the modern day fluid dynamics problems. Progress in computer performance allows us to solve a complex flow field in practical CPU time. Commodity clusters are also gaining popularity as computational research platform for various CFD communities. This research focuses on evaluating and enhancing the performance of an in-house, unstructured, 3D CFD code on modern commodity clusters. The fundamental idea is to tune the codes to optimize the cache behavior of the node on commodity clusters to achieve enhanced code performance. Accordingly, this work presents discussion of various available techniques for data access optimization and detailed description of those which yielded improved code performance. These techniques were tested on various steady, unsteady, laminar, and turbulent test cases and the results are presented. The critical hardware parameters which influenced the code performance were identified. A detailed study investigating the effect of these parameters on the code performance was conducted and the results are presented. The successful single node improvements were also efficiently tested on parallel platform. The modified version of the code was also ported to different hardware architectures with successful results. Loop blocking is established as a predictor of code performance.

Increased scaling costs and lack of desired features is leading to the evolution of high-performance storage systems from centralized architectures and specialized hardware to decentralized, commodity storage clusters. Existing systems try to address storage cost and management issues at the filesystem level. Besides dictating the use of a specific filesystem, however, this approach leads to increased complexity and load imbalance towards the file-server side, which in turn increase costs to scale. In this thesis, we examine these problems at the block-level. This approach has several advantages, such as transparency, cost-efficiency, better resource utilization, simplicity and easier management. First of all, we explore the mechanisms, the merits, and the overheads associated with advanced metadata-intensive functionality at the block level, by providing versioning at the block level. We find that block-level versioning has low overhead and offers transparency and simplicity advantages over filesystem-based approaches. Secondly, we study the problem of providing extensibility required by diverse and changing application needs that may use a single storage system. We provide support for (i)adding desired functions as block-level extensions, and (ii)flexibly combining them to create modular I/O hierarchies. In this direction, we design, implement and evaluate an extensible block-level storage virtualization framework, Violin, with support for metadata-intensive functions. Extending Violin we build Orchestra, an extensible framework for cluster storage virtualization and scalable storage sharing at the block-level. We show that Orchestra's enhanced block interface can substantially simplify the design of higher-level storage services, such as cluster filesystems, while being scalable. Finally, we consider the problem of consistency and availability in decentralized commodity clusters. We propose RIBD, a novel storage system that provides support for handling both data and metadata consistency issues at the block layer. RIBD uses the notion of consistency intervals (CIs) to provide fine-grain consistency semantics on sequences of block level operations by means of a lightweight transactional mechanism. RIBD relies on Orchestra's virtualization mechanisms and uses a roll-back recovery mechanism based on low-overhead block-level versioning. We evaluate RIBD on a cluster of 24 nodes, and find that it performs comparably to two popular cluster filesystems, PVFS and GFS, while offering stronger consistency guarantees.

Increased scaling costs and lack of desired features is leading to the evolution of high-performance storage systems from centralized architectures and specialized hardware to decentralized, commodity storage clusters. Existing systems try to address storage cost and management issues at the filesystem level. Besides dictating the use of a specific filesystem, however, this approach leads to increased complexity and load imbalance towards the file-server side, which in turn increase costs to scale. In this thesis, we examine these problems at the block-level. This approach has several advantages, such as transparency, cost-efficiency, better resource utilization, simplicity and easier management. First of all, we explore the mechanisms, the merits, and the overheads associated with advanced metadata-intensive functionality at the block level, by providing versioning at the block level. We find that block-level versioning has low overhead and offers transparency and simplicity advantages over filesystem-based approaches. Secondly, we study the problem of providing extensibility required by diverse and changing application needs that may use a single storage system. We provide support for (i)adding desired functions as block-level extensions, and (ii)flexibly combining them to create modular I/O hierarchies. In this direction, we design, implement and evaluate an extensible block-level storage virtualization framework, Violin, with support for metadata-intensive functions. Extending Violin we build Orchestra, an extensible framework for cluster storage virtualization and scalable storage sharing at the block-level. We show that Orchestra's enhanced block interface can substantially simplify the design of higher-level storage services, such as cluster filesystems, while being scalable. Finally, we consider the problem of consistency and availability in decentralized commodity clusters. We propose RIBD, a novel storage system that provides support for handling both data and metadata consistency issues at the block layer. RIBD uses the notion of consistency intervals (CIs) to provide fine-grain consistency semantics on sequences of block level operations by means of a lightweight transactional mechanism. RIBD relies on Orchestra's virtualization mechanisms and uses a roll-back recovery mechanism based on low-overhead block-level versioning. We evaluate RIBD on a cluster of 24 nodes, and find that it performs comparably to two popular cluster filesystems, PVFS and GFS, while offering stronger consistency guarantees.