GPUMap: A Transparently GPU-Accelerated Map Function

As GPGPU computing becomes more popular, it will be used to tackle a wider range of problems. However, due to the current state of GPGPU programming, programmers are typically required to be familiar with the architecture of the GPU in order to effectively program it. Fortunately, there are software packages that attempt to simplify GPGPU programming in higher-level languages such as Java and Python. However, these software packages do not attempt to abstract the GPU-acceleration process completely. Instead, they require programmers to be somewhat familiar with the traditional GPGPU programming model which involves some understanding of GPU threads and kernels. In addition, prior to using these software packages, programmers are required to transform the data they would like to operate on into arrays of primitive data. Typically, such software packages restrict the use of object-oriented programming when implementing the code to operate on this data. This thesis presents GPUMap, which is a proof-of-concept GPU-accelerated map function for Python. GPUMap aims to hide all the details of the GPU from the programmer, and allows the programmer to accelerate programs written in normal Python code that operate on arbitrarily nested objects using a majority of Python syntax. Using GPUMap, certain types of Python programs are able to be accelerated up to 100 times over normal Python code. There are also software packages that provide simplified GPU acceleration to distributed computing frameworks such as MapReduce and Spark. Unfortunately, these packages do not provide a completely abstracted GPU programming experience, which conflicts with the purpose of the distributed computing frameworks: to abstract the underlying distributed system. This thesis also presents GPU-accelerated RDD (GPURDD), which is a type of Spark Resilient Distributed Dataset (RDD) which incorporates GPUMap into its map, filter, and foreach methods in order to allow Spark applicatons to make use of the abstracted GPU acceleration provided by GPUMap

Thrill: High-performance algorithmic distributed batch data processing with C++

We present the design and a first performance evaluation of Thrill -- a prototype of a general purpose big data processing framework with a convenient data-flow style programming interface. Thrill is somewhat similar to Apache Spark and Apache Flink with at least two main differences. First, Thrill is based on C++ which enables performance advantages due to direct native code compilation, a more cache-friendly memory layout, and explicit memory management. In particular, Thrill uses template meta-programming to compile chains of subsequent local operations into a single binary routine without intermediate buffering and with minimal indirections. Second, Thrill uses arrays rather than multisets as its primary data structure which enables additional operations like sorting, prefix sums, window scans, or combining corresponding fields of several arrays (zipping). We compare Thrill with Apache Spark and Apache Flink using five kernels from the HiBench suite. Thrill is consistently faster and often several times faster than the other frameworks. At the same time, the source codes have a similar level of simplicity and abstractio

PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development

This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. In the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on automatic, relational-database style optimization to figure out how to stage distributed computations. However, in the small, PlinyCompute presents the capable systems programmer with a persistent object data model and API (the "PC object model") and associated memory management system that has been designed from the ground-up for high performance, distributed, data-intensive computing. This contrasts with most other Big Data systems, which are constructed on top of the Java Virtual Machine (JVM), and hence must at least partially cede performance-critical concerns such as memory management (including layout and de/allocation) and virtual method/function dispatch to the JVM. This hybrid approach---declarative in the large, trusting the programmer's ability to utilize PC object model efficiently in the small---results in a system that is ideal for the development of reusable, data-intensive tools and libraries. Through extensive benchmarking, we show that implementing complex objects manipulation and non-trivial, library-style computations on top of PlinyCompute can result in a speedup of 2x to more than 50x or more compared to equivalent implementations on Spark.Comment: 48 pages, including references and Appendi

Facilitating High Performance Code Parallelization

With the surge of social media on one hand and the ease of obtaining information due to cheap sensing devices and open source APIs on the other hand, the amount of data that can be processed is as well vastly increasing. In addition, the world of computing has recently been witnessing a growing shift towards massively parallel distributed systems due to the increasing importance of transforming data into knowledge in today’s data-driven world. At the core of data analysis for all sorts of applications lies pattern matching. Therefore, parallelizing pattern matching algorithms should be made efficient in order to cater to this ever-increasing abundance of data. We propose a method that automatically detects a user’s single threaded function call to search for a pattern using Java’s standard regular expression library, and replaces it with our own data parallel implementation using Java bytecode injection. Our approach facilitates parallel processing on different platforms consisting of shared memory systems (using multithreading and NVIDIA GPUs) and distributed systems (using MPI and Hadoop). The major contributions of our implementation consist of reducing the execution time while at the same time being transparent to the user. In addition to that, and in the same spirit of facilitating high performance code parallelization, we present a tool that automatically generates Spark Java code from minimal user-supplied inputs. Spark has emerged as the tool of choice for efficient big data analysis. However, users still have to learn the complicated Spark API in order to write even a simple application. Our tool is easy to use, interactive and offers Spark’s native Java API performance. To the best of our knowledge and until the time of this writing, such a tool has not been yet implemented

Efficient Parallel and Distributed Algorithms for GIS Polygon Overlay Processing

Polygon clipping is one of the complex operations in computational geometry. It is used in Geographic Information Systems (GIS), Computer Graphics, and VLSI CAD. For two polygons with n and m vertices, the number of intersections can be O(nm). In this dissertation, we present the first output-sensitive CREW PRAM algorithm, which can perform polygon clipping in O(log n) time using O(n + k + k\u27) processors, where n is the number of vertices, k is the number of intersections, and k\u27 is the additional temporary vertices introduced due to the partitioning of polygons. The current best algorithm by Karinthi, Srinivas, and Almasi does not handle self-intersecting polygons, is not output-sensitive and must employ O(n^2) processors to achieve O(log n) time. The second parallel algorithm is an output-sensitive PRAM algorithm based on Greiner-Hormann algorithm with O(log n) time complexity using O(n + k) processors. This is cost-optimal when compared to the time complexity of the best-known sequential plane-sweep based algorithm for polygon clipping. For self-intersecting polygons, the time complexity is O(((n + k) log n log log n)/p) using p In addition to these parallel algorithms, the other main contributions in this dissertation are 1) multi-core and many-core implementation for clipping a pair of polygons and 2) MPI-GIS and Hadoop Topology Suite for distributed polygon overlay using a cluster of nodes. Nvidia GPU and CUDA are used for the many-core implementation. The MPI based system achieves 44X speedup while processing about 600K polygons in two real-world GIS shapefiles 1) USA Detailed Water Bodies and 2) USA Block Group Boundaries) within 20 seconds on a 32-node (8 cores each) IBM iDataPlex cluster interconnected by InfiniBand technology

Supporting Efficient Database Processing in Mapreduce

Ph.DDOCTOR OF PHILOSOPH

Productive Programming Systems for Heterogeneous Supercomputers

The majority of today's scientific and data analytics workloads are still run on relatively energy inefficient, heavyweight, general-purpose processing cores, often referred to in the literature as latency-oriented architectures. The flexibility of these architectures and the programmer aids included (e.g. large and deep cache hierarchies, branch prediction logic, pre-fetch logic) makes them flexible enough to run a wide range of applications fast. However, we have started to see growth in the use of lightweight, simpler, energy-efficient, and functionally constrained cores. These architectures are commonly referred to as throughput-oriented. Within each shared memory node, the computational backbone of future throughput-oriented HPC machines will consist of large pools of lightweight cores. The first wave of throughput-oriented computing came in the mid 2000's with the use of GPUs for general-purpose and scientific computing. Today we are entering the second wave of throughput-oriented computing, with the introduction of NVIDIA Pascal GPUs, Intel Knights Landing Xeon Phi processors, the Epiphany Co-Processor, the Sunway MPP, and other throughput-oriented architectures that enable pre-exascale computing. However, while the majority of the FLOPS in designs for future HPC systems come from throughput-oriented architectures, they are still commonly paired with latency-oriented cores which handle management functions and lightweight/un-parallelizable computational kernels. Hence, most future HPC machines will be heterogeneous in their processing cores. However, the heterogeneity of future machines will not be limited to the processing elements. Indeed, heterogeneity will also exist in the storage, networking, memory, and software stacks of future supercomputers. As a result, it will be necessary to combine many different programming models and libraries in a single application. How to do so in a programmable and well-performing manner is an open research question. This thesis addresses this question using two approaches. First, we explore using managed runtimes on HPC platforms. As a result of their high-level programming models, these managed runtimes have a long history of supporting data analytics workloads on commodity hardware, but often come with overheads which make them less common in the HPC domain. Managed runtimes are also not supported natively on throughput-oriented architectures. Second, we explore the use of a modular programming model and work-stealing runtime to compose the programming and scheduling of multiple third-party HPC libraries. This approach leverages existing investment in HPC libraries, unifies the scheduling of work on a platform, and is designed to quickly support new programming model and runtime extensions. In support of these two approaches, this thesis also makes novel contributions in tooling for future supercomputers. We demonstrate the value of checkpoints as a software development tool on current and future HPC machines, and present novel techniques in performance prediction across heterogeneous cores

Концепция единого пространства геомагнитных данных

Задача мониторинга параметров геомагнитного поля и его вариаций преимущественно решается сетью магнитных обсерваторий и вариационных станций, однако значимым препятствием при обработке и анализе получаемых таким образом данных наряду с их пространственной анизотропией являются пропуски (или полное отсутствие) достоверных значений и частичное несоответствие установленному формату. Неоднородность и аномальность данных исключает (существенно усложняет) возможность их автоматической интеграции и применения к ним инструментария для частотного анализа. Известные решения по интеграции разнородных геомагнитных данных базируются преимущественно на модели консолидации и лишь частично решают данную проблему. Получаемые в результате наборы данных, как правило, не соответствуют требованиям IAGA (International Association of Geomagnetism and Aeronomy — Международной ассоциации геомагнетизма и аэрономии), рекомендуемым к представлению результатов геомагнитных наблюдений. При этом пропуски во временных рядах устраняются известными средствами обработки геомагнитных данных путем исключения отсутствующих или аномальных значений из конечной выборки, что, очевидно, может привести как к потере актуальной информации о ходе изменения параметров геомагнитного поля и его вариаций, нарушению шага дискретизации, так и к неоднородности временного ряда. Предлагается подход к созданию единого пространства геомагнитных данных, основанный на комбинировании моделей консолидации и федерализации, включающий предварительную обработку исходных временных рядов с опционально доступной процедурой их восстановления и верификации, ориентированный на применение технологий облачных вычислений и иерархического формата с целью повышения вычислительной скорости обработки больших объемов данных и, как следствие, обеспечивающий получение пользователями более качественных и однородных данных

Концепция единого пространства геомагнитных данных

. As is known, today the problem of geomagnetic field and its variations parameters monitoring is solved mainly by a network of magnetic observatories and variational stations, but a significant obstacle in the processing and analysis of the data thus obtained, along with their spatial anisotropy, are omissions or reliable inconsistency with the established format. Heterogeneity and anomalousness of the data excludes (significantly complicates) the possibility of their automatic integration and the application of frequency analysis tools to them. Known solutions for the integration of heterogeneous geomagnetic data are mainly based on the consolidation model and only partially solve the problem. The resulting data sets, as a rule, do not meet the requirements for real-time information systems, may include outliers, and omissions in the time series of geomagnetic data are eliminated by excluding missing or anomalous values from the final sample, which can obviously lead to both to the loss of relevant information, violation of the discretization step, and to heterogeneity of the time series. The paper proposes an approach to creating an integrated space of geomagnetic data based on a combination of consolidation and federalization models, including preliminary processing of the original time series with an optionally available procedure for their recovery and verification, focused on the use of cloud computing technologies and hierarchical format and processing speed of large amounts of data and, as a result, providing users with better and more homogeneous data.Задача мониторинга параметров геомагнитного поля и его вариаций преимущественно решается сетью магнитных обсерваторий и вариационных станций, однако значимым препятствием при обработке и анализе получаемых таким образом данных наряду с их пространственной анизотропией являются пропуски (или полное отсутствие) достоверных значений и частичное несоответствие установленному формату. Неоднородность и аномальность данных исключает (существенно усложняет) возможность их автоматической интеграции и применения к ним инструментария для частотного анализа. Известные решения по интеграции разнородных геомагнитных данных базируются преимущественно на модели консолидации и лишь частично решают данную проблему. Получаемые в результате наборы данных, как правило, не соответствуют требованиям IAGA (International Association of Geomagnetism and Aeronomy — Международной ассоциации геомагнетизма и аэрономии), рекомендуемым к представлению результатов геомагнитных наблюдений. При этом пропуски во временных рядах устраняются известными средствами обработки геомагнитных данных путем исключения отсутствующих или аномальных значений из конечной выборки, что, очевидно, может привести как к потере актуальной информации о ходе изменения параметров геомагнитного поля и его вариаций, нарушению шага дискретизации, так и к неоднородности временного ряда. Предлагается подход к созданию единого пространства геомагнитных данных, основанный на комбинировании моделей консолидации и федерализации, включающий предварительную обработку исходных временных рядов с опционально доступной процедурой их восстановления и верификации, ориентированный на применение технологий облачных вычислений и иерархического формата с целью повышения вычислительной скорости обработки больших объемов данных и, как следствие, обеспечивающий получение пользователями более качественных и однородных данных