On bulk-synchronous distributed-memory parallel processing of relational-database transactions

This paper describes two parallel algorithms for the eÆcient processing of relational database transactions and presents a performance analysis of them. These algorithms are built upon the bulk-synchronous parallel model of computation. The well-de ned structure of this model enabled us to evaluate their performance by using an implementation independent and yet em- pirical approach which includes the e ects of synchronization, communication and computation. The analysis reveals that the algorithm which borrows ideas from optimistic parallel discrete event simulation achieves better performance than the classical approach for synchronizing con- current transactions on a distributed memory system.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

Gunrock: A High-Performance Graph Processing Library on the GPU

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We evaluate Gunrock on five key graph primitives and show that Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives, and better performance than any other GPU high-level graph library.Comment: 14 pages, accepted by PPoPP'16 (removed the text repetition in the previous version v5

Gunrock: GPU Graph Analytics

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing (TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance Graph Processing Library on the GPU

Towards Parallel Programming Models for Predictability

Future embedded systems for performance-demanding applications will be massively parallel. High performance tasks will be parallel programs, running on several cores, rather than single threads running on single cores. For hard real-time applications, WCETs for such tasks must be bounded. Low-level parallel programming models, based on concurrent threads, are notoriously hard to use due to their inherent nondeterminism. Therefore the parallel processing community has long considered high-level parallel programming models, which restrict the low-level models to regain determinism. In this position paper we argue that such parallel programming models are beneficial also for WCET analysis of parallel programs. We review some proposed models, and discuss their influence on timing predictability. In particular we identify data parallel programming as a suitable paradigm as it is deterministic and allows current methods for WCET analysis to be extended to parallel code. GPUs are increasingly used for high performance applications: we discuss a current GPU architecture, and we argue that it offers a parallel platform for compute-intensive applications for which it seems possible to construct precise timing models. Thus, a promising route for the future is to develop WCET analyses for data-parallel software running on GPUs

High Performance Computing Algorithms for Accelerating Peptide Identification from Mass-Spectrometry Data Using Heterogeneous Supercomputers

Fast and accurate identification of peptides and proteins from the mass spectrometry (MS) data is a critical problem in modern systems biology. Database peptide search is the most commonly used computational method to identify peptide sequences from the MS data. In this method, giga-bytes of experimentally generated MS data are compared against tera-byte sized databases of theoretically simulated MS data resulting in a compute- and data-intensive problem requiring days or weeks of computational times on desktop machines. Existing serial and high performance computing (HPC) algorithms strive to accelerate and improve the computational efficiency of the search, but exhibit sub-optimal performances due to their inefficient parallelization models, low resource utilization and high overhead costs

Index structures for distributed text databases

The Web has became an obiquitous resource for distributed computing making it relevant to investigate new ways of providing efficient access to services available at dedicated sites. Efficiency is an ever-increasing demand which can be only satisfied with the development of parallel algorithms which are efficient in practice. This tutorial paper focuses on the design, analysis and implementation of parallel algorithms and data structures for widely-used text database applications on the Web. In particular we describe parallel algorithms for inverted files and suffix arrays structures that are suitable for implementing search engines. Algorithmic design is effected on top of the BSP model of parallel computing. This model ensures portability across diverse parallel architectures ranging from clusters to super-computers.Facultad de Informátic

Index structures for distributed text databases

The Web has became an obiquitous resource for distributed computing making it relevant to investigate new ways of providing efficient access to services available at dedicated sites. Efficiency is an ever-increasing demand which can be only satisfied with the development of parallel algorithms which are efficient in practice. This tutorial paper focuses on the design, analysis and implementation of parallel algorithms and data structures for widely-used text database applications on the Web. In particular we describe parallel algorithms for inverted files and suffix arrays structures that are suitable for implementing search engines. Algorithmic design is effected on top of the BSP model of parallel computing. This model ensures portability across diverse parallel architectures ranging from clusters to super-computers.Facultad de Informátic

On bulk-synchronous distributed-memory parallel processing of relational-database transactions

This paper describes two parallel algorithms for the eÆcient processing of relational database transactions and presents a performance analysis of them. These algorithms are built upon the bulk-synchronous parallel model of computation. The well-de ned structure of this model enabled us to evaluate their performance by using an implementation independent and yet em- pirical approach which includes the e ects of synchronization, communication and computation. The analysis reveals that the algorithm which borrows ideas from optimistic parallel discrete event simulation achieves better performance than the classical approach for synchronizing con- current transactions on a distributed memory system.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

GPU Usage for Parallel Functions and Contacts in Modelica

This thesis investigates two ways of incorporating GPUs in Modelica. The first by automatically generating GPU code for Modelica functions, and the second by using GPU accelerated external code for a contact handling package. Automatic parallelization of functions is desired, as it can potentially accelerate large simulations significantly. Special patterns of nested for-loops in Modelica code are recognized and translated into CUDA kernel functions. Inline integration allows a broader spectrum of models to take advantage of the parallelization, by reducing CPU-GPU transfers. The prototype has been tested and achieved a speed-up factor of up to five compared to the CPU. The contact handling package is capable of handling both complex contact behavior between arbitrarily shaped bodies and large DEM-like simulations, something which Modelica is currently lacking. Attempts to accelerate the package with GPUs were made, with partial success for the broad phase. The package uses Morton encoding for the broad phase, and the narrow phase is based on CSG intersection with BSP trees. Contact response is calculated using a volume dependent method, taking friction, damping and multiple contact points into account. The capability of the package was demonstrated by the fact that both complex contact behavior such as the inversion of the Tippe Top toy and tens of thousands of colliding spheres could be simulated.One of the key components in our modern society is the ability to simulate. By simulations, the industry can design new cars, phones, aircrafts etc., without having to go through prototype after prototype, allowing us the cheap and high-tech products most of us rely on in our day-to-day life. However, good as simulations are today there are still severe limitations on what can be simulated. Two of the largest limiting factors are how hard simulations are to design and how long they take to run. The first of these problems are tackled by the Modelica programming language, which is designed for easy set-up of simulations. We have attacked both these problems by using GPUs to speed-up Modelica simulations more than 5 times, and extending the capability of Modelica to handle collisions between objects