Tiling Optimization For Nested Loops On Gpus

Optimizing nested loops has been considered as an important topic and widely studied in parallel programming. With the development of GPU architectures, the performance of these computations can be significantly boosted with the massively parallel hardware. General matrix-matrix multiplication is a typical example where executing such an algorithm on GPUs outperforms the performance obtained on other multicore CPUs. However, achieving ideal performance on GPUs usually requires a lot of human effort to manage the massively parallel computation resources. Therefore, the efficient implementation of optimizing nested loops on GPUs became a popular topic in recent years. We present our work based on the tiling strategy in this dissertation to address three kinds of popular problems. Different kinds of computations bring in different latency issues where dependencies in the computation may result in insufficient parallelism and the performance of computations without dependencies may be degraded due to intensive memory accesses. In this thesis, we tackle the challenges for each kind of problem and believe that other computations performed in nested loops can also benefit from the presented techniques. We improve a parallel approximation algorithm for the problem of scheduling jobs on parallel identical machines to minimize makespan with a high-dimensional tiling method. The algorithm is designed and optimized for solving this kind of problem efficiently on GPUs. Because the algorithm is based on a higher-dimensional dynamic programming approach, where dimensionality refers to the number of variables in the dynamic programming equation characterizing the problem, the existing implementation suffers from the pain of dimensionality and cannot fully utilize GPU resources. We design a novel data-partitioning technique to accelerate the higher-dimensional dynamic programming component of the algorithm. Both the load imbalance and exceeding memory capacity issues are addressed in our GPU solution. We present performance results to demonstrate how our proposed design improves the GPU utilization and makes it possible to solve large higher-dimensional dynamic programming problems within the limited GPU memory. Experimental results show that the GPU implementation achieves up to 25X speedup compared to the best existing OpenMP implementation. In addition, we focus on optimizing wavefront parallelism on GPUs. Wavefront parallelism is a well-known technique for exploiting the concurrency of applications that execute nested loops with uniform data dependencies. Recent research on such applications, which range from sequence alignment tools to partial differential equation solvers, has used GPUs to benefit from the massively parallel computing resources. Wavefront parallelism faces the load imbalance issue because the parallelism is passing along the diagonal. The tiling method has been introduced as a popular solution to address this issue. However, the use of hyperplane tiles increases the cost of synchronization and leads to poor data locality. In this paper, we present a highly optimized implementation of the wavefront parallelism technique that harnesses the GPU architecture. A balanced workload and maximum resource utilization are achieved with an extremely low synchronization overhead. We design the kernel configuration to significantly reduce the minimum number of synchronizations required and also introduce an inter-block lock to minimize the overhead of each synchronization. We evaluate the performance of our proposed technique for four different applications: Sequence Alignment, Edit Distance, Summed-Area Table, and 2DSOR. The performance results demonstrate that our method achieves speedups of up to six times compared to the previous best-known hyperplane tiling-based GPU implementation. Finally, we extend the hyperplane tiling to high order 2D stencil computations. Unlike wavefront parallelism that has dependence in the spatial dimension, dependence remains only across two adjacent time steps along the temporal dimension in stencil computations. Even if the no-dependence property significantly increases the parallelism obtained in the spatial dimensions, full parallelism may not be efficient on GPUs. Due to the limited cache capacity owned by each streaming multiprocessor, full parallelism can be obtained on global memory only, which has high latency to access. Therefore, the tiling technique can be applied to improve the memory efficiency by caching the small tiled blocks. Because the widely studied tiling methods, like overlapped tiling and split tiling, have considerable computation overhead caused by load imbalance or extra operations, we propose a time skewed tiling method, which is designed upon the GPU architecture. We work around the serialized computation issue and coordinate the intra-tile parallelism and inter-tile parallelism to minimize the load imbalance caused by pipelined processing. Moreover, we address the high-order stencil computations in our development, which has not been comprehensively studied. The proposed method achieves up to 3.5X performance improvement when the stencil computation is performed on a Moore neighborhood pattern

Runtime-aware architectures

In the last few years, the traditional ways to keep the increase of hardware performance to the rate predicted by the Moore’s Law have vanished. When uni-cores were the norm, hardware design was decoupled from the software stack thanks to a well defined Instruction Set Architecture (ISA). This simple interface allowed developing applications without worrying too much about the underlying hardware, while hardware designers were able to aggressively exploit instruction-level parallelism (ILP) in superscalar processors. Current multi-cores are designed as simple symmetric multiprocessors (SMP) on a chip. However, we believe that this is not enough to overcome all the problems that multi-cores face. The runtime system of the parallel programming model has to drive the design of future multi-cores to overcome the restrictions in terms of power, memory, programmability and resilience that multi-cores have. In the paper, we introduce an approach towards a Runtime-Aware Architecture (RAA), a massively parallel architecture designed from the runtime’s perspective.This work has been partially supported by the European Research Council under the European Union’s 7th FP, ERC Grant Agreement number 321253, by the Spanish Ministry of Science and Innovation under grant TIN2012-34557 and by the HiPEAC Network of Excellence. M. Moreto has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI- 2012-15047, and M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Co-fund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243).Peer ReviewedPostprint (author's final draft

On-the-fly tracing for data-centric computing : parallelization, workflow and applications

As data-centric computing becomes the trend in science and engineering, more and more hardware systems, as well as middleware frameworks, are emerging to handle the intensive computations associated with big data. At the programming level, it is crucial to have corresponding programming paradigms for dealing with big data. Although MapReduce is now a known programming model for data-centric computing where parallelization is completely replaced by partitioning the computing task through data, not all programs particularly those using statistical computing and data mining algorithms with interdependence can be re-factorized in such a fashion. On the other hand, many traditional automatic parallelization methods put an emphasis on formalism and may not achieve optimal performance with the given limited computing resources. In this work we propose a cross-platform programming paradigm, called on-the-fly data tracing , to provide source-to-source transformation where the same framework also provides the functionality of workflow optimization on larger applications. Using a big-data approximation computations related to large-scale data input are identified in the code and workflow and a simplified core dependence graph is built based on the computational load taking in to account big data. The code can then be partitioned into sections for efficient parallelization; and at the workflow level, optimization can be performed by adjusting the scheduling for big-data considerations, including the I/O performance of the machine. Regarding each unit in both source code and workflow as a model, this framework enables model-based parallel programming that matches the available computing resources. The techniques used in model-based parallel programming as well as the design of the software framework for both parallelization and workflow optimization as well as its implementations with multiple programming languages are presented in the dissertation. Then, the following experiments are performed to validate the framework: i) the benchmarking of parallelization speed-up using typical examples in data analysis and machine learning (e.g. naive Bayes, k-means) and ii) three real-world applications in data-centric computing with the framework are also described to illustrate the efficiency: pattern detection from hurricane and storm surge simulations, road traffic flow prediction and text mining from social media data. In the applications, it illustrates how to build scalable workflows with the framework along with performance enhancements

Omphale: Streamlining the Communication for Jobs in a Multi Processor System on Chip

Our Multi Processor System on Chip (MPSoC) template provides processing tiles that are connected via a network on chip. A processing tile contains a processing unit and a Scratch Pad Memory (SPM). This paper presents the Omphale tool that performs the first step in mapping a job, represented by a task graph, to such an MPSoC, given the SPM sizes as constraints. Furthermore a memory tile is introduced. The result of Omphale is a Cyclo Static DataFlow (CSDF) model and a task graph where tasks communicate via sliding windows that are located in circular buffers. The CSDF model is used to determine the size of the buffers and the communication pattern of the data. A buffer must fit in the SPM of the processing unit that is reading from it, such that low latency access is realized with a minimized number of stall cycles. If a task and its buffer exceed the size of the SPM, the task is examined for additional parallelism or the circular buffer is partly located in a memory tile. This results in an extended task graph that satisfies the SPM size constraints

SIMD@OpenMP : a programming model approach to leverage SIMD features

SIMD instruction sets are a key feature in current general purpose and high performance architectures. SIMD instructions apply in parallel the same operation to a group of data, commonly known as vector. A single SIMD/vector instruction can, thus, replace a sequence of scalar instructions. Consequently, the number of instructions can be greatly reduced leading to improved execution times. However, SIMD instructions are not widely exploited by the vast majority of programmers. In many cases, taking advantage of these instructions relies on the compiler. Nevertheless, compilers struggle with the automatic vectorization of codes. Advanced programmers are then compelled to exploit SIMD units by hand, using low-level hardware-specific intrinsics. This approach is cumbersome, error prone and not portable across SIMD architectures. This thesis targets OpenMP to tackle the underuse of SIMD instructions from three main areas of the programming model: language constructions, compiler code optimizations and runtime algorithms. We choose the Intel Xeon Phi coprocessor (Knights Corner) and its 512-bit SIMD instruction set for our evaluation process. We make four contributions aimed at improving the exploitation of SIMD instructions in this scope. Our first contribution describes a compiler vectorization infrastructure suitable for OpenMP. This infrastructure targets for-loops and whole functions. We define a set of attributes for expressions that determine how the code is vectorized. Our vectorization infrastructure also implements support for several advanced vector features. This infrastructure is proven to be effective in the vectorization of complex codes and it is the basis upon which we build the following two contributions. The second contribution introduces a proposal to extend OpenMP 3.1 with SIMD parallelism. Essential parts of this work have become key features of the SIMD proposal included in OpenMP 4.0. We define the "simd" and "simd for" directives that allow programmers to describe SIMD parallelism of loops and whole functions. Furthermore, we propose a set of optional clauses that leads the compiler to generate a more efficient vector code. These SIMD extensions improve the programming efficiency when exploiting SIMD resources. Our evaluation on the Intel Xeon Phi coprocessor shows that our SIMD proposal allows the compiler to efficiently vectorize codes poorly or not vectorized automatically with the Intel C/C++ compiler. In the third contribution, we propose a vector code optimization that enhances overlapped vector loads. These vector loads redundantly read from memory scalar elements already loaded by other vector loads. Our vector code optimization improves the memory usage of these accesses by means of building a vector register cache and exploiting register-to-register instructions. Our proposal also includes a new clause (overlap) in the context of the SIMD extensions for OpenMP of our first contribution. This new clause allows enabling, disabling and tuning this optimization on demand. The last contribution tackles the exploitation of SIMD instructions in the OpenMP barrier and reduction primitives. We propose a new combined barrier and reduction tree scheme specifically designed to make the most of SIMD instructions. Our barrier algorithm takes advantage of simultaneous multi-threading technology (SMT) and it utilizes SIMD memory instructions in the synchronization process. The four contributions of this thesis are an important step in the direction of a more common and generalized use of SIMD instructions. Our work is having an outstanding impact on the whole OpenMP community, ranging from users of the programming model to compiler and runtime implementations. Our proposals in the context of OpenMP improves the programmability of the programming model, the overhead of runtime services and the execution time of applications by means of a better use of SIMD.Los juegos de instrucciones SIMD son un componente clave en las arquitecturas de propósito general y de alto rendimiento actuales. Estas instrucciones aplican en paralelo la misma operación a un conjunto de datos, conocido como vector. Una instrucción SIMD/vectorial puede sustituir una secuencia de instrucciones escalares. Así, el número de instrucciones puede ser reducido considerablemente, dando lugar a mejores tiempos de ejecución. No obstante, las instrucciones SIMD no son explotadas ampliamente por la mayoría de programadores. En general, beneficiarse de estas instrucciones depende del compilador. Sin embargo, los compiladores tienen dificultades con la vectorización automática de códigos por lo que los programadores avanzados se ven obligados a explotar las unidades SIMD manualmente, empleando intrínsecas de bajo nivel específicas del hardware. Esta aproximación es costosa, propensa a errores y no portable entre arquitecturas. Esta tesis se centra en el modelo de programación OpenMP para abordar el poco uso de las instrucciones SIMD desde tres áreas: construcciones del lenguaje, optimizaciones de código del compilador y algoritmos del runtime. Hemos escogido el coprocesador Intel Xeon Phi (Knights Corner) y su juego de instrucciones SIMD de 512 bits para nuestra evaluación. Realizamos cuatro contribuciones para mejorar la explotación de las instrucciones SIMD en este ámbito. Nuestra primera contribución describe una infraestructura de vectorización de compilador adecuada para OpenMP. Esta infraestructura tiene como objetivo la vectorización de bucles y funciones. Para ello definimos un conjunto de atributos que determina como se vectoriza el código. Nuestra evaluación demuestra la efectividad de esta infraestructura en la vectorización de códigos complejos. Esta infraestructura es la base de las dos propuestas siguientes. En la segunda contribución proponemos una extensión SIMD para de OpenMP 3.1. Partes esenciales de este trabajo se han convertido en características clave de la propuesta sobre SIMD incluida en OpenMP 4.0. Definimos las directivas ‘simd’ y ‘simd for’ que permiten a los programadores describir paralelismo SIMD de bucles y funciones. Además, proponemos un conjunto de cláusulas opcionales que permiten que el compilador genere código vectorial más eficiente. Nuestra evaluación muestra que nuestra propuesta SIMD permite al compilador vectorizar eficientemente códigos pobremente o no vectorizados automáticamente con el compilador Intel C/C++

A metadata-enhanced framework for high performance visual effects

This thesis is devoted to reducing the interactive latency of image processing computations in visual effects. Film and television graphic artists depend upon low-latency feedback to receive a visual response to changes in effect parameters. We tackle latency with a domain-specific optimising compiler which leverages high-level program metadata to guide key computational and memory hierarchy optimisations. This metadata encodes static and dynamic information about data dependence and patterns of memory access in the algorithms constituting a visual effect – features that are typically difficult to extract through program analysis – and presents it to the compiler in an explicit form. By using domain-specific information as a substitute for program analysis, our compiler is able to target a set of complex source-level optimisations that a vendor compiler does not attempt, before passing the optimised source to the vendor compiler for lower-level optimisation. Three key metadata-supported optimisations are presented. The first is an adaptation of space and schedule optimisation – based upon well-known compositions of the loop fusion and array contraction transformations – to the dynamic working sets and schedules of a runtimeparameterised visual effect. This adaptation sidesteps the costly solution of runtime code generation by specialising static parameters in an offline process and exploiting dynamic metadata to adapt the schedule and contracted working sets at runtime to user-tunable parameters. The second optimisation comprises a set of transformations to generate SIMD ISA-augmented source code. Our approach differs from autovectorisation by using static metadata to identify parallelism, in place of data dependence analysis, and runtime metadata to tune the data layout to user-tunable parameters for optimal aligned memory access. The third optimisation comprises a related set of transformations to generate code for SIMT architectures, such as GPUs. Static dependence metadata is exploited to guide large-scale parallelisation for tens of thousands of in-flight threads. Optimal use of the alignment-sensitive, explicitly managed memory hierarchy is achieved by identifying inter-thread and intra-core data sharing opportunities in memory access metadata. A detailed performance analysis of these optimisations is presented for two industrially developed visual effects. In our evaluation we demonstrate up to 8.1x speed-ups on Intel and AMD multicore CPUs and up to 6.6x speed-ups on NVIDIA GPUs over our best hand-written implementations of these two effects. Programmability is enhanced by automating the generation of SIMD and SIMT implementations from a single programmer-managed scalar representation

A Near-Infrared Stellar Census of the Blue Compact Dwarf Galaxy VII~Zw~403

We present near-infrared single-star photometry for the low-metallicity Blue Compact Dwarf galaxy VII~Zw~403. We achieve limiting magnitudes of F110W~$\approx$~25.5 and F160W~$\approx$~24.5 using one of the NICMOS cameras with the HST equivalents of the ground-based J and H filters. The data have a high photometric precision (0.1 mag) and are $>95$% complete down to magnitudes of about 23, far deeper than previous ground-based studies in the near-IR. The color-magnitude diagram contains about 1000 point sources. We provide a preliminary transformation of the near-IR photometry into the ground system...Comment: Accepted for publication by the AJ, preprint has 49 pages, 2 tables, and 16 figure

High-Level GPU Programming: Domain-Specific Optimization and Inference

When writing computer software one is often forced to balance the need for high run-time performance with high programmer productivity. By using a high-level language it is often possible to cut development times, but this typically comes at the cost of reduced run-time performance. Using a lower-level language, programs can be made very efficient but at the cost of increased development time. Real-time computer graphics is an area where there are very high demands on both performance and visual quality. Typically, large portions of such applications are written in lower-level languages and also rely on dedicated hardware, in the form of programmable graphics processing units (GPUs), for handling computationally demanding rendering algorithms. These GPUs are parallel stream processors, specialized towards computer graphics, that have computational performance more than a magnitude higher than corresponding CPUs. This has revolutionized computer graphics and also led to GPUs being used to solve more general numerical problems, such as fluid and physics simulation, protein folding, image processing, and databases. Unfortunately, the highly specialized nature of GPUs has also made them difficult to program. In this dissertation we show that GPUs can be programmed at a higher level, while maintaining performance, compared to current lower-level languages. By constructing a domain-specific language (DSL), which provides appropriate domain-specific abstractions and user-annotations, it is possible to write programs in a more abstract and modular manner. Using knowledge of the domain it is possible for the DSL compiler to generate very efficient code. We show that, by experiment, the performance of our DSLs is equal to that of GPU programs written by hand using current low-level languages. Also, control over the trade-offs between visual quality and performance is retained. In the papers included in this dissertation, we present domain-specific languages targeted at numerical processing and computer graphics, respectively. These DSL have been implemented as embedded languages in Python, a dynamic programming language that provide a rich set of high-level features. In this dissertation we show how these features can be used to facilitate the construction of embedded languages

Robust nonlinear control of vectored thrust aircraft

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations