We discuss a programming language for real-time audio signal processing that is embedded in the functional language Haskell and uses the Low-Level Virtual Machine as back-end. With that framework we can code with the comfort and type safety of Haskell while achieving maximum efficiency of fast inner loops and full vectorisation. This way Haskell becomes a valuable alternative to special purpose signal processing languages.
Our goal is to make use of the elegance of programming for signal processing. Our work is driven by the experience that today compiled code cannot compete with traditional signal processing packages written in C. There has been a lot of progress in recent years, most notably the improved support for arrays without overhead, the elimination of temporary arrays ( fusion ) and the Data-Parallel Haskell project @cite_21 that aims at utilising multiple cores of modern processors for array oriented data processing. However there is still a considerable gap in performance between idiomatic code and idiomatic C code. A recent development is an LLVM -backend for the GHC Glasgow Haskell Compiler . that adds all of the low-level optimisations of LLVM to GHC . However we still need some tuning of the high-level optimisation and a support for processor vector types in order to catch up with our EDSL method.
@cite_21
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Papers on functional language implementations frequently set the goal of achieving performance "comparable to C", and sometimes report results comparing benchmark results to concrete C implementations of the same problem. A key pair of questions for such comparisons is: what C program to compare to, and what C compiler to compare with? In a 2012 paper, [9] compare naive serial C implementations of a range of throughput-oriented benchmarks to best-optimized implementations parallelized on a six-core machine and demonstrate an average 23X (up to 53X) speedup. Even accounting for thread parallel speedup, these results demonstrate a substantial performance gap between naive and tuned C code. In this current paper, we choose a subset of the benchmarks studied by to port to Haskell. We measure performance of these Haskell benchmarks compiled with the standard Glasgow Haskell Compiler and with our experimental Intel Labs Haskell Research Compiler and report results as compared to our best reconstructions of the algorithms used by Results are reported as measured both on an Intel Xeon E5-4650 32-core machine, and on an Intel Xeon Phi co-processor. We hope that this study provides valuable data on the concrete performance of Haskell relative to C. If you want to program a parallel computer, a purely functional language like Haskell is a promising starting point. Since the language is pure, it is by-default safe for parallel evaluation, whereas imperative languages are by-default unsafe. But that doesn't make it easy! Indeed it has proved quite difficult to get robust, scalable performance increases through parallel functional programming, especially as the number of processors increases. A particularly promising and well-studied approach to employing large numbers of processors is to use data parallelism. Blelloch's pioneering work on NESL showed that it was possible to combine a rather flexible programming model (nested data parallelism) with a fast, scalable execution model (flat data parallelism). In this talk I will describe Data Parallel Haskell, which embodies nested data parallelism in a modern, general-purpose language, implemented in a state-of-the-art compiler, GHC. I will focus particularly on the vectorisation transformation, which transforms nested to flat data parallelism, and I hope to present performance numbers. We describe LLVM (low level virtual machine), a compiler framework designed to support transparent, lifelong program analysis and transformation for arbitrary programs, by providing high-level information to compiler transformations at compile-time, link-time, run-time, and in idle time between runs. LLVM defines a common, low-level code representation in static single assignment (SSA) form, with several novel features: a simple, language-independent type-system that exposes the primitives commonly used to implement high-level language features; an instruction for typed address arithmetic; and a simple mechanism that can be used to implement the exception handling features of high-level languages (and setjmp longjmp in C) uniformly and efficiently. The LLVM compiler framework and code representation together provide a combination of key capabilities that are important for practical, lifelong analysis and transformation of programs. To our knowledge, no existing compilation approach provides all these capabilities. We describe the design of the LLVM representation and compiler framework, and evaluate the design in three ways: (a) the size and effectiveness of the representation, including the type information it provides; (b) compiler performance for several interprocedural problems; and (c) illustrative examples of the benefits LLVM provides for several challenging compiler problems. As main memory grows, query performance is more and more determined by the raw CPU costs of query processing itself. The classical iterator style query processing technique is very simple and exible, but shows poor performance on modern CPUs due to lack of locality and frequent instruction mispredictions. Several techniques like batch oriented processing or vectorized tuple processing have been proposed in the past to improve this situation, but even these techniques are frequently out-performed by hand-written execution plans. In this work we present a novel compilation strategy that translates a query into compact and efficient machine code using the LLVM compiler framework. By aiming at good code and data locality and predictable branch layout the resulting code frequently rivals the performance of hand-written C++ code. We integrated these techniques into the HyPer main memory database system and show that this results in excellent query performance while requiring only modest compilation time. Dynamic, interpreted languages, like Python, are attractive for domain-experts and scientists experimenting with new ideas. However, the performance of the interpreter is often a barrier when scaling to larger data sets. This paper presents a just-in-time compiler for Python that focuses in scientific and array-oriented computing. Starting with the simple syntax of Python, Numba compiles a subset of the language into efficient machine code that is comparable in performance to a traditional compiled language. In addition, we share our experience in building a JIT compiler using LLVM[1]. Software-controlled data prefetching offers the potential for bridging the ever-increasing speed gap between the memory subsystem and today's high-performance processors. While prefetching has enjoyed considerable success in array-based numeric codes, its potential in pointer-based applications has remained largely unexplored. This paper investigates compiler-based prefetching for pointer-based applications---in particular, those containing recursive data structures. We identify the fundamental problem in prefetching pointer-based data structures and propose a guideline for devising successful prefetching schemes. Based on this guideline, we design three prefetching schemes, we automate the most widely applicable scheme (greedy prefetching) in an optimizing research compiler, and we evaluate the performance of all three schemes on a modern superscalar processor similar to the MIPS R10000. Our results demonstrate that compiler-inserted prefetching can significantly improve the execution speed of pointer-based codes---as much as 45 for the applications we study. In addition, the more sophisticated algorithms (which we currently perform by hand, but which might be implemented in future compilers) can improve performance by as much as twofold. Compared with the only other compiler-based pointer prefetching scheme in the literature, our algorithms offer substantially better performance by avoiding unnecessary overhead and hiding more latency. Energy models can be constructed by characterizing the energy consumed by executing each instruction in a processor's instruction set. This can be used to determine how much energy is required to execute a sequence of assembly instructions, without the need to instrument or measure hardware. However, statically analyzing low-level program structures is hard, and the gap between the high-level program structure and the low-level energy models needs to be bridged. We have developed techniques for performing a static analysis on the intermediate compiler representations of a program. Specifically, we target LLVM IR, a representation used by modern compilers, including Clang. Using these techniques we can automatically infer an estimate of the energy consumed when running a function under different platforms, using different compilers. One of the challenges in doing so is that of determining an energy cost of executing LLVM IR program segments, for which we have developed two different approaches. When this information is used in conjunction with our analysis, we are able to infer energy formulae that characterize the energy consumption for a particular program. This approach can be applied to any languages targeting the LLVM toolchain, including C and XC or architectures such as ARM Cortex-M or XMOS xCORE, with a focus towards embedded platforms. Our techniques are validated on these platforms by comparing the static analysis results to the physical measurements taken from the hardware. Static energy consumption estimation enables energy-aware software development, without requiring hardware knowledge. Emerging GPU architectures for high performance computing are well suited to a data-parallel programming model. This paper presents preliminary work examining a programming methodology that provides Fortran programmers with access to these emerging systems. We use array constructs in Fortran to show how this infrequently exploited, standardised language feature is easily transformed to lower-level accelerator code. The transformations in ForOpenCL are based on a simple mapping from Fortran to OpenCL. We demonstrate, using a stencil code solving the shallow-water fluid equations, that the performance of the ForOpenCL compiler-generated transformations is comparable with that of hand-optimised OpenCL code. Nowadays compilers include tens or hundreds of optimization passes, which makes it difficult to find sequences of optimizations that achieve compiled code more optimized than the one obtained using typical compiler options such as -O2 and -O3. The problem involves both the selection of the compiler passes to use and their ordering in the compilation pipeline. The improvement achieved by the use of custom phase orders for each function can be significant, and thus important to satisfy strict requirements such as the ones present in high-performance embedded computing systems. In this paper we present a new and fast iterative approach to the phase selection and ordering challenges resulting in compiled code with higher performance than the one achieved with the standard optimization levels of the LLVM compiler. The obtained performance improvements are comparable with the ones achieved by other iterative approaches while requiring considerably less time and resources. Our approach is based on sampling over a graph representing transitions between compiler passes. We performed a number of experiments targeting the LEON3 microarchitecture using the Clang LLVM 3.7 compiler, considering 140 LLVM passes and a set of 42 representative signal and image processing C functions. An exhaustive cross-validation shows our new exploration method is able to achieve a geometric mean performance speedup of 1.28x over the best individually selected -OX flag when considering 100,000 iterations; versus geometric mean speedups from 1.16x to 1.25x obtained with state-of-the-art iterative methods not using the graph. From the set of exploration methods tested, our new method is the only one consistently finding compiler sequences that result in performance improvements when considering 100 or less exploration iterations. Specifically, it achieved geometric mean speedups of 1.08x and 1.16x for 10 and 100 iterations, respectively. For many scientific applications, significant time is spent in tuning codes for a particular high-performance architecture. Tuning approaches range from the relatively nonintrusive (e.g., by using compiler options) to extensive code modifications that attempt to exploit specific architecture features. Intrusive techniques often result in code changes that are not easily reversible, and can negatively impact readability, maintainability, and performance on different architectures. We introduce an extensible annotation-based empirical tuning system called Orio that is aimed at improving both performance and productivity. It allows software developers to insert annotations in the form of structured comments into their source code to trigger a number of low-level performance optimizations on a specified code fragment. To maximize the performance tuning opportunities, the annotation processing infrastructure is designed to support both architecture-independent and architecture-specific code optimizations. Given the annotated code as input, Orio generates many tuned versions of the same operation and empirically evaluates the alternatives to select the best performing version for production use. We have also enabled the use of the Pluto automatic parallelization tool in conjunction with Orio to generate efficient OpenMP-based parallel code. We describe our experimental results involving a number of computational kernels, including dense array and sparse matrix operations. This paper presents the interesting observation that by performing fewer of the optimizations available in a standard compiler optimization level such as -02, while preserving their original ordering, significant savings can be achieved in both execution time and energy consumption. This observation has been validated on two embedded processors, namely the ARM Cortex-M0 and the ARM Cortex-M3, using two different versions of the LLVM compilation framework; v3.8 and v5.0. Experimental evaluation with 71 embedded benchmarks demonstrated performance gains for at least half of the benchmarks for both processors. An average execution time reduction of 2.4 and 5.3 was achieved across all the benchmarks for the Cortex-M0 and Cortex-M3 processors, respectively, with execution time improvements ranging from 1 up to 90 over the -02. The savings that can be achieved are in the same range as what can be achieved by the state-of-the-art compilation approaches that use iterative compilation or machine learning to select flags or to determine phase orderings that result in more efficient code. In contrast to these time consuming and expensive to apply techniques, our approach only needs to test a limited number of optimization configurations, less than 64, to obtain similar or even better savings. Furthermore, our approach can support multi-criteria optimization as it targets execution time, energy consumption and code size at the same time. Recent compilers offer a vast number of multilayered optimizations targeting different code segments of an application. Choosing among these optimizations can significantly impact the performance of the code being optimized. The selection of the right set of compiler optimizations for a particular code segment is a very hard problem, but finding the best ordering of these optimizations adds further complexity. Finding the best ordering represents a long standing problem in compilation research, named the phase-ordering problem. The traditional approach of constructing compiler heuristics to solve this problem simply cannot cope with the enormous complexity of choosing the right ordering of optimizations for every code segment in an application. This article proposes an automatic optimization framework we call MiCOMP, which Mi tigates the Com piler P hase-ordering problem. We perform phase ordering of the optimizations in LLVM’s highest optimization level using optimization sub-sequences and machine learning. The idea is to cluster the optimization passes of LLVM’s O3 setting into different clusters to predict the speedup of a complete sequence of all the optimization clusters instead of having to deal with the ordering of more than 60 different individual optimizations. The predictive model uses (1) dynamic features, (2) an encoded version of the compiler sequence, and (3) an exploration heuristic to tackle the problem. Experimental results using the LLVM compiler framework and the Cbench suite show the effectiveness of the proposed clustering and encoding techniques to application-based reordering of passes, while using a number of predictive models. We perform statistical analysis on the results and compare against (1) random iterative compilation, (2) standard optimization levels, and (3) two recent prediction approaches. We show that MiCOMP’s iterative compilation using its sub-sequences can reach an average performance speedup of 1.31 (up to 1.51). Additionally, we demonstrate that MiCOMP’s prediction model outperforms the -O1, -O2, and -O3 optimization levels within using just a few predictions and reduces the prediction error rate down to only 5 . Overall, it achieves 90 of the available speedup by exploring less than 0.001 of the optimization space. Despite the apparent success of the Java Virtual Machine, its lackluster performance makes it ill-suited for many speed-critical applications. Although the latest just-in-time compilers and dedicated Java processors try to remedy this situation, optimized code compiled directly from a C program source is still considerably faster than software transported via Java byte-codes. This is true even if the Java byte-codes are subsequently further translated into native code. In this paper, we claim that these performance penalties are not a necessary consequence of machine-independence, but related to Java's particular intermediate representation and runtime architecture. We have constructed a prototype and are further developing a software transportability scheme founded on a tree-based alternative to Java byte-codes. This tree-based intermediate representation is not only twice as compact as Java byte-codes, but also contains more high-level information, some of which is critical for advanced code optimizations. Our architecture not only provides on-the-fly code generation from this intermediate representation, but also continuous re-optimization of the existing code-base by a low-priority background process. The re-optimization process is guided by up-to-the-minute profiling data, leading to superior runtime performance. X10 is a modern object-oriented language designed for productivity and performance in concurrent and distributed systems. In this setting, dependent types offer significant opportunities for detecting design errors statically, documenting design decisions, eliminating costly run-time checks (e.g., for array bounds, null values), and improving the quality of generated code. We present the design and implementation of constrained types, a natural, simple, clean, and expressive extension to object-oriented programming: A type C c names a class or interface C and a constraint c on the immutable state of C and in-scope final variables. Constraints may also be associated with class definitions (representing class invariants) and with method and constructor definitions (representing preconditions). Dynamic casting is permitted. The system is parametric on the underlying constraint system: the compiler supports a simple equality-based constraint system but, in addition, supports extension with new constraint systems using compiler plugins. Current proposals for adding gradual typing to JavaScript, such as Closure, TypeScript and Dart, forgo soundness to deal with issues of scale, code reuse, and popular programming patterns. We show how to address these issues in practice while retaining soundness. We design and implement a new gradual type system, prototyped for expediency as a 'Safe' compilation mode for TypeScript. Our compiler achieves soundness by enforcing stricter static checks and embedding residual runtime checks in compiled code. It emits plain JavaScript that runs on stock virtual machines. Our main theorem is a simulation that ensures that the checks introduced by Safe TypeScript (1) catch any dynamic type error, and (2) do not alter the semantics of type-safe TypeScript code. Safe TypeScript is carefully designed to minimize the performance overhead of runtime checks. At its core, we rely on two new ideas: differential subtyping, a new form of coercive subtyping that computes the minimum amount of runtime type information that must be added to each object; and an erasure modality, which we use to safely and selectively erase type information. This allows us to scale our design to full-fledged TypeScript, including arrays, maps, classes, inheritance, overloading, and generic types. We validate the usability and performance of Safe TypeScript empirically by type-checking and compiling around 120,000 lines of existing TypeScript source code. Although runtime checks can be expensive, the end-to-end overhead is small for code bases that already have type annotations. For instance, we bootstrap the Safe TypeScript compiler (90,000 lines including the base TypeScript compiler): we measure a 15 runtime overhead for type safety, and also uncover programming errors as type safety violations. We conclude that, at least during development and testing, subjecting JavaScript TypeScript programs to safe gradual typing adds significant value to source type annotations at a modest cost. We present the design and implementation of a SQL query processor that outperforms existing database systems and is written in just about 500 lines of Scala code -- a convincing case study that high-level functional programming can handily beat C for systems-level programming where the last drop of performance matters. The key enabler is a shift in perspective towards generative programming. The core of the query engine is an interpreter for relational algebra operations, written in Scala. Using the open-source LMS Framework (Lightweight Modular Staging), we turn this interpreter into a query compiler with very low effort. To do so, we capitalize on an old and widely known result from partial evaluation known as Futamura projections, which state that a program that can specialize an interpreter to any given input program is equivalent to a compiler. In this pearl, we discuss LMS programming patterns such as mixed-stage data structures (e.g. data records with static schema and dynamic field components) and techniques to generate low-level C code, including specialized data structures and data loading primitives. Retargetable C compilers are key components of today's embedded processor design platforms for quickly obtaining compiler support and performing early processor architecture exploration. The inherent problem of the retargetable compilation approach, though, is the well known trade-off between the compiler's flexibility and the quality of generated code. However, it can be circumvented by designing flexible, configurable code optimization techniques applicable to a certain range of target architectures. This paper focuses on target machines with predicated execution support which is wide-spread in deeply pipelined and highly parallel embedded processors used in next generation high-end video, multimedia and wireless devices. We present an efficient and quickly retargetable code optimization technique for predicated execution that is integrated into an industrial retargetable C compiler. Experimental results for several embedded processors demonstrate that the proposed technique is applicable to real-life target machines and that it produces significant code quality improvements for control intensive applications. Performance optimization for large-scale applications has recently become more important as computation continues to move towards data centers. Data-center applications are generally very large and complex, which makes code layout an important optimization to improve their performance. This has motivated recent investigation of practical techniques to improve code layout at both compile time and link time. Although post-link optimizers had some success in the past, no recent work has explored their benefits in the context of modern data-center applications. In this paper, we present BOLT, a post-link optimizer built on top of the LLVM framework. Utilizing sample-based profiling, BOLT boosts the performance of real-world applications even for highly optimized binaries built with both feedback-driven optimizations (FDO) and link-time optimizations (LTO). We demonstrate that post-link performance improvements are complementary to conventional compiler optimizations, even when the latter are done at a whole-program level and in the presence of profile information. We evaluated BOLT on both Facebook data-center workloads and open-source compilers. For data-center applications, BOLT achieves up to 8.0 performance speedups on top of profile-guided function reordering and LTO. For the GCC and Clang compilers, our evaluation shows that BOLT speeds up their binaries by up to 20.4 on top of FDO and LTO, and up to 52.1 if the binaries are built without FDO and LTO. Multi-core chips from Intel and AMD offer a dramatic boost in speed and responsiveness, and plenty of opportunities for multiprocessing on ordinary desktop computers. But they also present a challenge: More than ever, multithreading is a requirement for good performance. This guide explains how to maximize the benefits of these processors through a portable C++ library that works on Windows, Linux, Macintosh, and Unix systems. With it, you'll learn how to use Intel Threading Building Blocks (TBB) effectively for parallel programming -- without having to be a threading expert. Written by James Reinders, Chief Evangelist of Intel Software Products, and based on the experience of Intel's developers and customers, this book explains the key tasks in multithreading and how to accomplish them with TBB in a portable and robust manner. With plenty of examples and full reference material, the book lays out common patterns of uses, reveals the gotchas in TBB, and gives important guidelines for choosing among alternatives in order to get the best performance. You'll learn how Intel Threading Building Blocks: * Enables you to specify tasks instead of threads for better portability, easier programming, more understandable source code, and better performance and scalability in general * Focuses on the goal of parallelizing computationally intensive work to deliver high-level solutions * Is compatible with other threading packages, and doesn't force you to pick one package for your entire program * Emphasizes scalable, data-parallel programming, which allows program performance to increase as you add processors * Relies on generic programming, which enables you to write the best possible algorithms with the fewest constraints Any C++ programmer who wants to write an application to run on a multi-core system will benefit from this book. TBB is also very approachable for a C programmer or a C++ programmer without much experience with templates. Best of all, you don't need experience with parallel programming or multi-core processors to use this book. Prefetching data ahead of use has the potential to tolerate the grow ing processor-memory performance gap by overlapping long latency memory accesses with useful computation. While sophisti cated prefetching techniques have been automated for limited domains, such as scientific codes that access dense arrays in loop nests, a similar level of success has eluded general-purpose pro grams, especially pointer-chasing codes written in languages such as C and C++. We address this problem by describing, implementing and evaluating a dynamic prefetching scheme. Our technique runs on stock hardware, is completely automatic, and works for general-purpose programs, including pointer-chasing codes written in weakly-typed languages, such as C and C++. It operates in three phases. First, the profiling phase gathers a temporal data reference profile from a running program with low-overhead. Next, the profiling is turned off and a fast analysis algorithm extracts hot data streams, which are data reference sequences that frequently repeat in the same order, from the temporal profile. Then, the system dynamically injects code at appropriate program points to detect and prefetch these hot data streams. Finally, the process enters the hibernation phase where no profiling or analysis is performed, and the program continues to execute with the added prefetch instructions. At the end of the hibernation phase, the program is de-optimized to remove the inserted checks and prefetch instructions, and control returns to the profiling phase. For long-running programs, this profile, analyze and optimize, hibernate, cycle will repeat multiple times. Our initial results from applying dynamic prefetching are promising, indicating overall execution time improvements of 5.19 for several memory-performance-limited SPECint2000 benchmarks running their largest (ref) inputs.