Loop unrolling is a widely adopted loop transformation, commonly used for enabling subsequent optimizations. Straight-line-code vectorization (SLP) is an optimization that benefits from unrolling. SLP converts isomorphic instruction sequences into vector code. Since unrolling generates repeatead isomorphic instruction sequences, it enables SLP to vectorize more code. However, most production compilers apply these optimizations independently and uncoordinated. Unrolling is commonly tuned to avoid code bloat, not maximizing the potential for vectorization, leading to missed vectorization opportunities.
We are proposing VALU, a novel loop unrolling heuristic that takes vectorization into account when making unrolling decisions. Our heuristic is powered by an analysis that estimates the potential benefit of SLP vectorization for the unrolled version of the loop. Our heuristic then selects the unrolling factor that maximizes the utilization of the vector units. VALU also forwards the vectorizable code to SLP, allowing it to bypass its greedy search for vectorizable seed instructions, exposing more vectorization opportunities.
Our evaluation on a production compiler shows that VALU uncovers many vectorization opportunities that were missed by the default loop unroller and vectorizers. This results in more vectorized code and significant performance speedups for 17 of the kernels of the TSVC benchmarks suite, reaching up to 2× speedup over the already highly optimized -O3. Our evaluation on full benchmarks from FreeBench and MiBench shows that VALU results in a geo-mean speedup of 1.06×.
Annotations and assertions capturing static program properties are ubiquitous, from robust software engineering to safety-critical or secure code. These may be functional or non-functional properties of control and data flow, memory usage, I/O and real time. We propose an approach to encode, translate, and preserve the semantics of both functional and non-functional properties along the optimizing compilation of C to machine code. The approach involves (1) capturing and translating source-level properties through lowering passes and intermediate representations, such that data and control flow optimizations will preserve their consistency with the transformed program, and (2) carrying properties and their translation as debug information down to machine code. Our experiments using LLVM validate the soundness, expressiveness and efficiency of the approach, considering a reference suite of functional properties as well as established security properties and applications hardened against side-channel attacks.
Existing precise context-sensitive heap analyses do not scale well for large OO programs. Further, identifying the right context abstraction becomes quite intriguing as two of the most popular categories of context abstractions (call-site- and object-sensitive) lead to theoretically incomparable precision. In this paper, we address this problem by first doing a detailed comparative study (in terms of precision and efficiency) of the existing approaches, both with and without heap cloning. In addition, we propose novel context abstractions that lead to a new sweet-spot in the arena.
We first enhance the precision of level-summarized relevant value (LSRV) contexts (a highly scalable abstraction with precision matching that of call-site-sensitivity) using heap cloning. Then, motivated by the resultant scalability, we propose the idea of mixing various context abstractions, and add the advantages of k-object-sensitive analyses to LSRV contexts, in an efficient manner. The resultant context abstraction, which we call lsrvkobjH, also leads to a novel connection between the two broad variants of otherwise incomparable context-sensitive analyses. Our evaluation shows that the newer proposals not only enhance the precision of both LSRV contexts and object-sensitive analyses (to perform control-flow analysis of Java programs), but also scale well to large programs.
Pointer analysis is widely used as a base for different kinds of static analyses and compiler optimizations. Designing a scalable pointer analysis with acceptable precision for use in production compilers is still an open question. Modern object oriented languages like Java and Scala promote abstractions and code reuse, both of which make it difficult to achieve precision. Collection data structures are an example of a pervasively used component in such languages. But analyzing collection implementations with full context sensitivity leads to prohibitively long analysis times.
We use semantic models to reduce the complex internal implementation of, e.g., a collection to a small and concise model. Analyzing the model with context sensitivity leads to precise results with only a modest increase in analysis time. The models must be written manually, which is feasible because a model method usually consists of only a few statements. Our implementation in GraalVM Native Image shows a rise in useful precision (1.35X rise in the number of checkcast statements that can be elided over the default analysis configuration) with a manageable performance cost (19% rise in analysis time).
Program profiling is widely used to measure run-time execution properties---for example, the frequency of method and statement execution. Such profiling could be applied to deployed software to gain performance insights about the behavior of many instances of the analyzed software. However, such data gathering raises privacy concerns: for example, it reveals whether (and how often) a software user accesses a particular software functionality. There is growing interest in adding privacy protections for many categories of data analyses, but such techniques have not been studied sufficiently for program event profiling.
We propose the design of privacy-preserving event frequency profiling for deployed software. Each instance of the targeted software gathers its own event frequency profile and then randomizes it. The resulting noisy data has well-defined privacy properties, characterized via the powerful machinery of differential privacy. After gathering this data from many software instances, the profiling infrastructure computes estimates of population-wide frequencies while adjusting for the effects of the randomization. The approach employs static analysis to determine constraints that must hold in all valid run-time profiles, and uses quadratic programming to reduce the error of the estimates under these constraints. Our experiments study different choices for randomization and the resulting effects on the accuracy of frequency estimates. Our conclusion is that well-designed solutions can achieve both high accuracy and principled privacy-by-design for the fundamental problem of event frequency profiling.
Array-based programming languages have shown significant promise for improving performance of column-based in-memory database systems, allowing elegant representation of query execution plans that are also amenable to standard compiler optimization techniques. Use of loop fusion, however, is not straightforward, due to the complexity of built-in functions for implementing complex database operators. In this work, we apply a compiler approach to optimize SQL query execution plans that are expressed in an array-based intermediate representation. We analyze this code to determine shape properties of the data being processed, and use a subsequent optimization phase to fuse multiple database operators into single, compound operations, reducing the need for separate computation and storage of intermediate values. Experimental results on a range of TPC-H queries show that our fusion technique is effective in generating efficient code, improving query time over a baseline system.
We studied robust quantization of deep neural networks (DNNs) for embedded devices. Existing compression techniques often generate DNNs that are sensitive to external errors. Because embedded devices may be affected by external lights and outside weather, DNNs running on those devices must be robust to such errors. For robust quantization of DNNs, we formulate an optimization problem that finds the bit width for each layer minimizing the robustness loss. To efficiently find the solution, we design a dynamic programming based algorithm, called Qed. We also propose an incremental algorithm, Q* that quickly finds a reasonably robust quantization and then gradually improves it. We have evaluated Qed and Q* with three DNN models (LeNet, AlexNet, and VGG-16) and with Gaussian random errors and realistic errors. For comparison, we also evaluate universal quantization that uses equal bit width for all layers and Deep Compression, a weight-sharing based compression technique. When tested with increasing size of errors, Qed most robustly gives correct inference output. Even if a DNN is optimized for robustness, its quantizations may not be robust unless Qed is used. Moreover, we evaluate Q* for its trade off in execution time and robustness. In one tenth of Qed’s execution time, Q* gives a quantization 98% as robust as the one by Qed.
Usage of high-level intermediate representations promises the generation of fast code from a high-level description, improving the productivity of developers while achieving the performance traditionally only reached with low-level programming approaches.
High-level IRs come in two flavors: 1) domain-specific IRs designed only for a specific application area; or 2) generic high-level IRs that can be used to generate high-performance code across many domains. Developing generic IRs is more challenging but offers the advantage of reusing a common compiler infrastructure across various applications.
In this paper, we extend a generic high-level IR to enable efficient computation with sparse data structures. Crucially, we encode sparse representation using reusable dense building blocks already present in the high-level IR. We use a form of dependent types to model sparse matrices in CSR format by expressing the relationship between multiple dense arrays explicitly separately storing the length of rows, the column indices, and the non-zero values of the matrix.
We achieve high-performance compared to sparse low-level library code using our extended generic high-level code generator. On an Nvidia GPU, we outperform the highly tuned Nvidia cuSparse implementation of spmv multiplication across 28 sparse matrices of varying sparsity on average by 1.7×.
In this paper, we propose a runtime framework that implements code multi-versioning and specialization to optimize and parallelize loop kernels that are invoked many times with varying parameters. These parameters may influence the code structure, the touched memory locations, the workload, and the runtime performance. They may also impact the validity of the parallelizing and optimizing polyhedral transformations that are applied on-the-fly.
For a target loop kernel and its associated parameters, a different optimizing and parallelizing transformation is evaluated at each invocation, among a finite set of transformations (multi-versioning and specialization). The best performing transformed code version is stored and indexed using its associated parameters. When every optimizing transformation has been evaluated, the best performing code version regarding the current parameters, which has been stored, is relaunched at next invocations (memoization).
Heterogeneous computers combine a general-purpose host processor with domain-specific programmable many-core accelerators, uniting high versatility with high performance and energy efficiency. While the host manages ever-more application memory, accelerators are designed to work mainly on their local memory. This difference in addressed memory leads to a discrepancy between the optimal address width of the host and the accelerator. Today 64-bit host processors are commonplace, but few accelerators exceed 32-bit addressable local memory, a difference expected to increase with 128-bit hosts in the exascale era. Managing this discrepancy requires support for multiple data models in heterogeneous compilers. So far, compiler support for multiple data models has not been explored, which hampers the programmability of such systems and inhibits their adoption.
In this work, we perform the first exploration of the feasibility and performance of implementing a mixed-data-model heterogeneous system. To support this, we present and evaluate the first mixed-data-model compiler, supporting arbitrary address widths on host and accelerator. To hide the inherent complexity and to enable high programmer productivity, we implement transparent offloading on top of OpenMP. The proposed compiler techniques are implemented in LLVM and evaluated on a 64+32-bit heterogeneous SoC. Results on benchmarks from the PolyBench-ACC suite show that memory can be transparently shared between host and accelerator at overheads below 0.7% compared to 32-bit-only execution, enabling mixed-data-model computers to execute at near-native performance.
Dynamic Binary Instrumentation (DBI) is a well-established approach for analysing the execution of applications at the level of machine code. DBI frameworks implement a runtime system capable of modifying running applications without access to their source code. These frameworks provide APIs used by DBI tools to plug in their specific analysis and instrumentation routines. However, the dynamic instrumentation needed by these DBI tools is either challenging to implement, and/or introduces a significant performance overhead.
An added complexity beyond the well studied scenario of x86 and x86-64, is that state-of-the-art Arm systems (i.e. Arm v8) introduced a distinct 64-bit execution mode with a new redesigned instruction set. Thus, Arm v8 is a computer architecture which contains three instruction sets. This further complicates the development of DBI tools which can work for both 32-bit Arm (includes the A32 and T32 instruction sets), and 64-bit (the A64 instruction set).
This paper presents the design of a novel DBI framework API that provides support both for portable (across A32, T32 and A64), and for native-code-level analysis and instrumentation, which can be intermixed freely. This API allows DBI tool developers to balance performance and productivity at a fine-grain level. The API is implemented on top of the MAMBO DBI system.
Building correct and efficient message-passing parallel programs still poses many challenges. The incorrect use of message-passing constructs can introduce deadlocks, and a bad task decomposition will not achieve good speedups. Current approaches focus either on correctness or efficiency, but limited work has been done on ensuring both. In this paper, we propose a new parallel programming framework, PAlg, which is a first-order language with participant annotations that ensures deadlock-freedom by construction. PAlg programs are coupled with an abstraction of their communication structure, a global type from the theory of multiparty session types (MPST). This global type serves as an output for the programmer to assess the efficiency of their achieved parallelisation. PAlg is implemented as an EDSL in Haskell, from which we: 1. compile to low-level message-passing C code; 2. compile to sequential C code, or interpret as sequential Haskell functions; and, 3. infer the communication protocol followed by the compiled message-passing program. We use the properties of global types to perform message reordering optimisations to the compiled C code. We prove the extensional equivalence of the compiled code, as well as protocol compliance. We achieve linear speedups on a shared-memory 12-core machine, and a speedup of 16 on a 2-node, 24-core NUMA.
Stateful packrat parsing is an algorithm for parsing syntaxes that have context-sensitive features. It is a well-known knowledge among researchers that the running time of stateful packrat parsing is linear for real-world grammars, as demonstrated in existing studies. However, we have found the cases in real-world grammars and tools that lead its running time to become exponential.
This paper proposes a new grammar, parsing expression grammar with variable bindings, and two parsing algorithms for the grammar, stateful packrat parsing with selected global states and stateful packrat parsing with conditional memoization. Our proposal overcomes the exponential behavior that appears in parsers and guarantees polynomial running time. The key idea behind our algorithms is to memoize the information relevant to the use of the global states in order to avoid memoizing the full global states. We implemented our algorithms as a parser generator and evaluated them on real-world grammars. Our evaluation shows that our algorithms significantly outperform an existing stateful packrat parsing algorithm in terms of both running time and space consumption. In particular, stateful packrat parsing with conditional memoization improves the running time and space consumption for malicious inputs that lead to exponential behavior with the existing algorithm by 260x and 217x, respectively, compared to the existing algorithm.
Unlike CPUs and GPUs, it is possible to use custom fixed-point data types, specified as a tuple (α, β), on FPGAs. The parameters α and β denote the number of integral and fractional bitwidths respectively. The power and area savings while performing arithmetic operations on fixed-point data types are well known to be significant over using floating-point data types.
In this paper, we propose a hybrid approach involving interval analysis and SMT solvers, for estimating integral bitwidths at different compute stages, in an image processing pipeline, specified using a domain-specific language (DSL) such as PolyMage. The DSL specification facilitates the compiler analysis to infer the underlying computational structure with ease. We also propose a simple and practical profile-driven greedy heuristic search technique for fractional bitwidth analysis. Using the Horn-Schunck Optical Flow benchmark program, we demonstrate where the conventional range analysis approaches fail, and how we overcome them using the hybrid technique proposed in this paper. The integral bitwidth estimates provided by the hybrid technique on the optical flow benchmark are up to 3x times better when compared with interval analysis.
Sparse linear algebra is central to many scientific programs, yet compilers fail to optimize it well. High-performance libraries are available, but adoption costs are significant. Moreover, libraries tie programs into vendor-specific software and hardware ecosystems, creating non-portable code.
In this paper, we develop a new approach based on our specification Language for implementers of Linear Algebra Computations (LiLAC). Rather than requiring the application developer to (re)write every program for a given library, the burden is shifted to a one-off description by the library implementer. The LiLAC-enabled compiler uses this to insert appropriate library routines without source code changes.
LiLAC provides automatic data marshaling, maintaining state between calls and minimizing data transfers. Appropriate places for library insertion are detected in compiler intermediate representation, independent of source languages.
We evaluated on large-scale scientific applications written in FORTRAN; standard C/C++ and FORTRAN benchmarks; and C++ graph analytics kernels. Across heterogeneous platforms, applications and data sets we show speedups of 1.1×to over 10×without user intervention.
Tree transformations are common in applications such as program rewriting in compilers. Using a series of simple transformations to build a more complex system can make the resulting software easier to understand, maintain, and reason about. Fusion strategies for combining such successive tree transformations promote this modularity, whilst mitigating the performance impact from increased numbers of tree traversals. However, it is important to ensure that fused transformations still perform their intended tasks. Existing approaches to fusing tree transformations tend to take an informal approach to soundness, or be too restrictive to consider the kind of transformations needed in a compiler. We use postconditions to define a more useful formal notion of successful fusion, namely postcondition-preserving fusion. We also present criteria that are sufficient to ensure postcondition-preservation and facilitate modular reasoning about the success of fusion.
In natural language processing, novel methods in deep learning, like recurrent neural networks (RNNs) on sequences of words, have been very successful. In contrast to natural languages, programming languages usually have a well-defined structure. With this structure compilers can reason about programs, using graphs such as abstract syntax trees (ASTs) or control-data flow graphs (CDFGs). In this paper, we argue that we should use these graph structures instead of sequences for learning compiler optimization tasks. To this end, we use graph neural networks (GNNs) for learning predictive compiler tasks on two representations based on ASTs and CDFGs. Experiments show that this improves upon the state-of-the-art in the task of heterogeneous OpenCL mapping, while providing orders of magnitude faster inference times, crucial for compiler optimizations. When testing on benchmark suites not included for training, our AST-based model significantly outperforms the state-of-the-art by over 12 percentage points in terms of accuracy. It is the only one to perform clearly better than a random mapping. On the task of predicting thread coarsening factors, we show that all of the methods fail to produce an overall speedup.
Most implementations of the C++ programming language generate binary executable code. However, interpreted execution of C++ sources has its own use cases as the Cling interpreter from CERN's ROOT project has shown. Some limitations are derived from the ODR (One Definition Rule) that rules out multiple definitions of entities within a single translation unit (TU). ODR is there to ensure uniform view of a given C++ entity across translation units. Ensuring uniform view of C++ entities helps when producing ABI compatible binaries. Interpreting C++ presumes a single ever-growing translation unit that define away some of the ODR use-cases. Therefore, it may well be desirable to relax the ODR and, consequently, to support the ability of developers to override any existing definition for a given declaration. This approach is especially well-suited for iterative prototyping. In this paper, we extend Cling, a Clang/LLVM-based C++ interpreter, to enable redefinitions of C++ entities at the prompt. To achieve this, top-level declarations are nested into inline namespaces and the translation unit lookup table is adjusted to invalidate previous definitions that would otherwise result in ambiguities. Formally, this technique refactors the code to an equivalent that does not violate the ODR, as each definition is nested in a different namespace. Furthermore, any previous definition that has been shadowed is still accessible by means of its fully-qualified name. A prototype implementation of the presented technique has been integrated into the Cling C++ interpreter, showing that our technique is feasible and usable.