The Potential of Synergistic Static, Dynamic and Speculative Loop Nest Optimizations for Automatic Parallelization

Research in automatic parallelization of loop-centric programs started with static analysis, then broadened its arsenal to include dynamic inspection-execution and speculative execution, the best results involving hybrid static-dynamic schemes. Beyond the detection of parallelism in a sequential program, scalable parallelization on many-core processors involves hard and interesting parallelism adaptation and mapping challenges. These challenges include tailoring data locality to the memory hierarchy, structuring independent tasks hierarchically to exploit multiple levels of parallelism, tuning the synchronization grain, balancing the execution load, decoupling the execution into thread-level pipelines, and leveraging heterogeneous hardware with specialized accelerators. The polyhedral framework allows to model, construct and apply very complex loop nest transformations addressing most of the parallelism adaptation and mapping challenges. But apart from hardware-specific, back-end oriented transformations (if-conversion, trace scheduling, value prediction), loop nest optimization has essentially ignored dynamic and speculative techniques. Research in polyhedral compilation recently reached a significant milestone towards the support of dynamic, data-dependent control flow. This opens a large avenue for blending dynamic analyses and speculative techniques with advanced loop nest optimizations. Selecting real-world examples from SPEC benchmarks and numerical kernels, we make a case for the design of synergistic static, dynamic and speculative loop transformation techniques. We also sketch the embedding of dynamic information, including speculative assumptions, in the heart of affine transformation search spaces

Vapor SIMD: Auto-Vectorize Once, Run Everywhere

International audienceJust-in-Time (JIT) compiler technology offers portability while facilitating target- and context-specific specialization. Single-Instruction-Multiple-Data (SIMD) hardware is ubiquitous and markedly diverse, but can be difficult for JIT compilers to efficiently target due to resource and budget constraints. We present our design for a synergistic auto-vectorizing compilation scheme. The scheme is composed of an aggressive, generic offline stage coupled with a lightweight, target-specific online stage. Our method leverages the optimized intermediate results provided by the first stage across disparate SIMD architectures from different vendors, having distinct characteristics ranging from different vector sizes, memory alignment and access constraints, to special computational idioms.We demonstrate the effectiveness of our design using a set of kernels that exercise innermost loop, outer loop, as well as straight-line code vectorization, all automatically extracted by the common offline compilation stage. This results in performance comparable to that provided by specialized monolithic offline compilers. Our framework is implemented using open-source tools and standards, thereby promoting interoperability and extendibility

Proceedings of the 3rd International Workshop on Polyhedral Compilation Techniques

IMPACT 2013 in Berlin, Germany (in conjuction with HiPEAC 2013) is the third workshop in a series of international workshops on polyhedral compilation techniques. The previous workshops were held in Chamonix, France (2011) in conjuction with CGO 2011 and Paris, France (2012) in conjuction with HiPEAC 2012

Runtime-adaptive generalized task parallelism

Multi core systems are ubiquitous nowadays and their number is ever increasing. And while, limited by physical constraints, the computational power of the individual cores has been stagnating or even declining for years, a solution to effectively utilize the computational power that comes with the additional cores is yet to be found. Existing approaches to automatic parallelization are often highly specialized to exploit the parallelism of specific program patterns, and thus to parallelize a small subset of programs only. In addition, frequently used invasive runtime systems prohibit the combination of different approaches, which impedes the practicality of automatic parallelization. In the following thesis, we show that specializing to narrowly defined program patterns is not necessary to efficiently parallelize applications coming from different domains. We develop a generalizing approach to parallelization, which, driven by an underlying mathematical optimization problem, is able to make qualified parallelization decisions taking into account the involved runtime overhead. In combination with a specializing, adaptive runtime system the approach is able to match and even exceed the performance results achieved by specialized approaches.Mehrkernsysteme sind heutzutage allgegenwärtig und finden täglich weitere Verbreitung. Und während, limitiert durch die Grenzen des physikalisch Machbaren, die Rechenkraft der einzelnen Kerne bereits seit Jahren stagniert oder gar sinkt, existiert bis heute keine zufriedenstellende Lösung zur effektiven Ausnutzung der gebotenen Rechenkraft, die mit der steigenden Anzahl an Kernen einhergeht. Existierende Ansätze der automatischen Parallelisierung sind häufig hoch spezialisiert auf die Ausnutzung bestimmter Programm-Muster, und somit auf die Parallelisierung weniger Programmteile. Hinzu kommt, dass häufig verwendete invasive Laufzeitsysteme die Kombination mehrerer Parallelisierungs-Ansätze verhindern, was der Praxistauglichkeit und Reichweite automatischer Ansätze im Wege steht. In der Ihnen vorliegenden Arbeit zeigen wir, dass die Spezialisierung auf eng definierte Programmuster nicht notwendig ist, um Parallelität in Programmen verschiedener Domänen effizient auszunutzen. Wir entwickeln einen generalisierenden Ansatz der Parallelisierung, der, getrieben von einem mathematischen Optimierungsproblem, in der Lage ist, fundierte Parallelisierungsentscheidungen unter Berücksichtigung relevanter Kosten zu treffen. In Kombination mit einem spezialisierenden und adaptiven Laufzeitsystem ist der entwickelte Ansatz in der Lage, mit den Ergebnissen spezialisierter Ansätze mitzuhalten, oder diese gar zu übertreffen.Part of the work presented in this thesis was performed in the context of the SoftwareCluster project EMERGENT (http://www.software-cluster.org). It was funded by the German Federal Ministry of Education and Research (BMBF) under grant no. “01IC10S01”. Later work has been supported, also by the German Federal Ministry of Education and Research (BMBF), through funding for the Center for IT-Security, Privacy and Accountability (CISPA) under grant no. “16KIS0344”

Vapor SIMD: Auto-Vectorize Once, Run Everywhere

International audienceJust-in-Time (JIT) compiler technology offers portability while facilitating target- and context-specific specialization. Single-Instruction-Multiple-Data (SIMD) hardware is ubiquitous and markedly diverse, but can be difficult for JIT compilers to efficiently target due to resource and budget constraints. We present our design for a synergistic auto-vectorizing compilation scheme. The scheme is composed of an aggressive, generic offline stage coupled with a lightweight, target-specific online stage. Our method leverages the optimized intermediate results provided by the first stage across disparate SIMD architectures from different vendors, having distinct characteristics ranging from different vector sizes, memory alignment and access constraints, to special computational idioms.We demonstrate the effectiveness of our design using a set of kernels that exercise innermost loop, outer loop, as well as straight-line code vectorization, all automatically extracted by the common offline compilation stage. This results in performance comparable to that provided by specialized monolithic offline compilers. Our framework is implemented using open-source tools and standards, thereby promoting interoperability and extendibility

Software caching techniques and hardware optimizations for on-chip local memories

Despite the fact that the most viable L1 memories in processors are caches, on-chip local memories have been a great topic of consideration lately. Local memories are an interesting design option due to their many benefits: less area occupancy, reduced energy consumption and fast and constant access time. These benefits are especially interesting for the design of modern multicore processors since power and latency are important assets in computer architecture today. Also, local memories do not generate coherency traffic which is important for the scalability of the multicore systems. Unfortunately, local memories have not been well accepted in modern processors yet, mainly due to their poor programmability. Systems with on-chip local memories do not have hardware support for transparent data transfers between local and global memories, and thus ease of programming is one of the main impediments for the broad acceptance of those systems. This thesis addresses software and hardware optimizations regarding the programmability, and the usage of the on-chip local memories in the context of both single-core and multicore systems. Software optimizations are related to the software caching techniques. Software cache is a robust approach to provide the user with a transparent view of the memory architecture; but this software approach can suffer from poor performance. In this thesis, we start optimizing traditional software cache by proposing a hierarchical, hybrid software-cache architecture. Afterwards, we develop few optimizations in order to speedup our hybrid software cache as much as possible. As the result of the software optimizations we obtain that our hybrid software cache performs from 4 to 10 times faster than traditional software cache on a set of NAS parallel benchmarks. We do not stop with software caching. We cover some other aspects of the architectures with on-chip local memories, such as the quality of the generated code and its correspondence with the quality of the buffer management in local memories, in order to improve performance of these architectures. Therefore, we run our research till we reach the limit in software and start proposing optimizations on the hardware level. Two hardware proposals are presented in this thesis. One is about relaxing alignment constraints imposed in the architectures with on-chip local memories and the other proposal is about accelerating the management of local memories by providing hardware support for the majority of actions performed in our software cache.Malgrat les memòries cau encara son el component basic pel disseny del subsistema de memòria, les memòries locals han esdevingut una alternativa degut a les seves característiques pel que fa a l’ocupació d’àrea, el seu consum energètic i el seu rendiment amb un temps d’accés ràpid i constant. Aquestes característiques son d’especial interès quan les properes arquitectures multi-nucli estan limitades pel consum de potencia i la latència del subsistema de memòria.Les memòries locals pateixen de limitacions respecte la complexitat en la seva programació, fet que dificulta la seva introducció en arquitectures multi-nucli, tot i els avantatges esmentats anteriorment. Aquesta tesi presenta un seguit de solucions basades en programari i maquinari específicament dissenyat per resoldre aquestes limitacions.Les optimitzacions del programari estan basades amb tècniques d'emmagatzematge de memòria cau suportades per llibreries especifiques. La memòria cau per programari és un sòlid mètode per proporcionar a l'usuari una visió transparent de l'arquitectura, però aquest enfocament pot patir d'un rendiment deficient. En aquesta tesi, es proposa una estructura jeràrquica i híbrida. Posteriorment, desenvolupem optimitzacions per tal d'accelerar l’execució del programari que suporta el disseny de la memòria cau. Com a resultat de les optimitzacions realitzades, obtenim que el nostre disseny híbrid es comporta de 4 a 10 vegades més ràpid que una implementació tradicional de memòria cau sobre un conjunt d’aplicacions de referencia, com son els “NAS parallel benchmarks”.El treball de tesi inclou altres aspectes de les arquitectures amb memòries locals, com ara la qualitat del codi generat i la seva correspondència amb la qualitat de la gestió de memòria intermèdia en les memòries locals, per tal de millorar el rendiment d'aquestes arquitectures. La tesi desenvolupa propostes basades estrictament en el disseny de nou maquinari per tal de millorar el rendiment de les memòries locals quan ja no es possible realitzar mes optimitzacions en el programari. En particular, la tesi presenta dues propostes de maquinari: una relaxa les restriccions imposades per les memòries locals respecte l’alineament de dades, l’altra introdueix maquinari específic per accelerar les operacions mes usuals sobre les memòries locals

Software-Oriented Data Access Characterization for Chip Multiprocessor Architecture Optimizations

The integration of an increasing amount of on-chip hardware in Chip-Multiprocessors (CMPs) poses a challenge of efficiently utilizing the on-chip resources to maximize performance. Prior research proposals largely rely on additional hardware support to achieve desirable tradeoffs. However, these purely hardware-oriented mechanisms typically result in more generic but less efficient approaches. A new trend is designing adaptive systems by exploiting and leveraging application-level information. In this work a wide range of applications are analyzed and remarkable data access behaviors/patterns are recognized to be useful for architectural and system optimizations. In particular, this dissertation work introduces software-based techniques that can be used to extract data access characteristics for cross-layer optimizations on performance and scalability. The collected information is utilized to guide cache data placement, network configuration, coherence operations, address translation, memory configuration, etc. In particular, an approach is proposed to classify data blocks into different categories to optimize an on-chip coherent cache organization. For applications with compile-time deterministic data access localities, a compiler technique is proposed to determine data partitions that guide the last level cache data placement and communication patterns for network configuration. A page-level data classification is also demonstrated to improve address translation performance. The successful utilization of data access characteristics on traditional CMP architectures demonstrates that the proposed approach is promising and generic and can be potentially applied to future CMP architectures with emerging technologies such as the Spin-transfer torque RAM (STT-RAM)

Reducing exception management overhead with software restart markers

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 181-196).Modern processors rely on exception handling mechanisms to detect errors and to implement various features such as virtual memory. However, these mechanisms are typically hardware-intensive because of the need to buffer partially-completed instructions to implement precise exceptions and enforce in-order instruction commit, often leading to issues with performance and energy efficiency. The situation is exacerbated in highly parallel machines with large quantities of programmer-visible state, such as VLIW or vector processors. As architects increasingly rely on parallel architectures to achieve higher performance, the problem of exception handling is becoming critical. In this thesis, I present software restart markers as the foundation of an exception handling mechanism for explicitly parallel architectures. With this model, the compiler is responsible for delimiting regions of idempotent code. If an exception occurs, the operating system will resume execution from the beginning of the region. One advantage of this approach is that instruction results can be committed to architectural state in any order within a region, eliminating the need to buffer those values. Enabling out-of-order commit can substantially reduce the exception management overhead found in precise exception implementations, and enable the use of new architectural features that might be prohibitively costly with conventional precise exception implementations. Additionally, software restart markers can be used to reduce context switch overhead in a multiprogrammed environment. This thesis demonstrates the applicability of software restart markers to vector, VLIW, and multithreaded architectures. It also contains an implementation of this exception handling approach that uses the Trimaran compiler infrastructure to target the Scale vectorthread architecture. I show that using software restart markers incurs very little performance overhead for vector-style execution on Scale.(cont.) Finally, I describe the Scale compiler flow developed as part of this work and discuss how it targets certain features facilitated by the use of software restart markersby Mark Jerome Hampton.Ph.D

Profile-directed specialisation of custom floating-point hardware

We present a methodology for generating floating-point arithmetic hardware designs which are, for suitable applications, much reduced in size, while still retaining performance and IEEE-754 compliance. Our system uses three key parts: a profiling tool, a set of customisable floating-point units and a selection of system integration methods. We use a profiling tool for floating-point behaviour to identify arithmetic operations where fundamental elements of IEEE-754 floating-point may be compromised, without generating erroneous results in the common case. In the uncommon case, we use simple detection logic to determine when operands lie outside the range of capabilities of the optimised hardware. Out-of-range operations are handled by a separate, fully capable, floatingpoint implementation, either on-chip or by returning calculations to a host processor. We present methods of system integration to achieve this errorcorrection. Thus the system suffers no compromise in IEEE-754 compliance, even when the synthesised hardware would generate erroneous results. In particular, we identify from input operands the shift amounts required for input operand alignment and post-operation normalisation. For operations where these are small, we synthesise hardware with reduced-size barrel-shifters. We also propose optimisations to take advantage of other profile-exposed behaviours, including removing the hardware required to swap operands in a floating-point adder or subtractor, and reducing the exponent range to fit observed values. We present profiling results for a range of applications, including a selection of computational science programs, Spec FP 95 benchmarks and the FFMPEG media processing tool, indicating which would be amenable to our method. Selected applications which demonstrate potential for optimisation are then taken through to a hardware implementation. We show up to a 45% decrease in hardware size for a floating-point datapath, with a correctable error-rate of less then 3%, even with non-profiled datasets

Indexed dependence metadata and its applications in software performance optimisation

To achieve continued performance improvements, modern microprocessor design is tending to concentrate an increasing proportion of hardware on computation units with less automatic management of data movement and extraction of parallelism. As a result, architectures increasingly include multiple computation cores and complicated, software-managed memory hierarchies. Compilers have difficulty characterizing the behaviour of a kernel in a general enough manner to enable automatic generation of efficient code in any but the most straightforward of cases. We propose the concept of indexed dependence metadata to improve application development and mapping onto such architectures. The metadata represent both the iteration space of a kernel and the mapping of that iteration space from a given index to the set of data elements that iteration might use: thus the dependence metadata is indexed by the kernel’s iteration space. This explicit mapping allows the compiler or runtime to optimise the program more efficiently, and improves the program structure for the developer. We argue that this form of explicit interface specification reduces the need for premature, architecture-specific optimisation. It improves program portability, supports intercomponent optimisation and enables generation of efficient data movement code. We offer the following contributions: an introduction to the concept of indexed dependence metadata as a generalisation of stream programming, a demonstration of its advantages in a component programming system, the decoupled access/execute model for C++ programs, and how indexed dependence metadata might be used to improve the programming model for GPU-based designs. Our experimental results with prototype implementations show that indexed dependence metadata supports automatic synthesis of double-buffered data movement for the Cell processor and enables aggressive loop fusion optimisations in image processing, linear algebra and multigrid application case studies