Dietmar Ebner

Compiler generation from structural architecture descriptions

With increasing complexity of modern embedded systems, the availability of highly optimizing compilers becomes more and more important. At the same time, application specific instruction-set processors (ASIPs) are used to fine-tune hardware platforms to the intended application, demanding the availability of retargetable components throughout thewhole tool chain. A very promising approach is to model the target architecture using a dedicated description language that is rich enough to generate hardware components and the required tool chain, e.g., assembler, linker, simulator, and compiler. In this work we present a new structural architecture description language (ADL) that is used to derive the architecture dependent components of a compiler backend - most notably an instruction selector based on tree pattern matching. We combine our backend with gcc, thereby opening up the way for a large number of readily available high level optimizations. Experimental results show that the automatically derived code generator is competitive in comparison to a handcrafted compiler backend.

High-performance language interoperability for scientific computing through Babel

High-performance scientific applications are usually built from software modules written in multiple programming languages. This raises the issue of language interoperability which involves making calls between languages, converting basic types, and bridging disparate programming models. Babel provides a feature-rich, extensible, high-performance solution to the language interoperability problem currently supporting C, C++, FORTRAN 77, Fortran 90/95, Fortran 2003/2008, Python, and Java. Babel supports object-oriented programming features and interface semantics with runtime enforcement. In addition to in-process language interoperability, Babel includes remote method invocation to support hybrid parallel and distributed computing paradigms.

Generalized instruction selection using SSA -graphs

Instruction selection is a well-studied compiler phase that translates the compilers intermediate representation of programs to a sequence of target-dependent machine instructions optimizing for various compiler objectives (e.g. speed and space). Most existing instruction selection techniques are limited to the scope of a single statement or a basic block and cannot cope with irregular instruction sets that are frequently found in embedded systems. We consider an optimal technique for instruction selection that uses Static Single Assignment (SSA) graphs as an intermediate representation of programs and employs the Partitioned Boolean Quadratic Problem (PBQP) for finding an optimal instruction selection. While existing approaches are limited to instruction patterns that can be expressed in a simple tree structure, we consider complex patterns producing multiple results at the same time including pre/post increment addressing modes, div-mod instructions, and SIMD extensions frequently found in embedded systems. Although both instruction selection on SSA-graphs and PBQP are known to be NP-complete, the problem can be solved efficiently - even for very large instances. Our approach has been implemented in LLVM for an embedded ARMv5 architecture. Extensive experiments show speedups of up to 57% on typical DSP kernels and up to 10% on SPECINT 2000 and MiBench benchmarks. All of the test programs could be compiled within less than half a minute using a heuristic PBQP solver that solves 99.83% of all instances optimally.

Progressive spill code placement

Register allocation has gained renewed attention in the recent past. Several authors propose a separation of the problem into decoupled sub-tasks including spilling, allocation, assignment, and coalescing. This approach is largely motivated by recent advances in SSA-based register allocation that suggest that a decomposition does not significantly degrade the overall allocation quality. The algorithmic challenges of intra-procedural spilling have been neglected so far and very crude heuristics were employed. In this work, (1) we introduce the constrained min-cut (CMC) problem for solving the spilling problem, (2) we provide an integer linear program formulation for computing an optimal solution of CMC, and (3) we devise a progressive Lagrangian solver that is viable for production compilers. Our experiments with Spec2k and MiBench show that optimal solutions are feasible, even for very large programs, and that heuristics leave significant potential behind for small register files.

Label number maximization in the slider model

We consider the NP-hard label number maximization problem lnm: Given a set of rectangular labels, each of which belongs to a point feature in the plane, the task is to find a labeling for a largest subset of the labels. A labeling is a placement such that none of the labels overlap and each is placed so that its boundary touches the corresponding point feature. The purpose of this paper is twofold: We present a new force-based simulated annealing algorithm to heuristically solve the problem and we provide the results of a very thorough experimental comparison of the best known labeling methods on widely used benchmark sets. The design of our new method has been guided by the goal to produce labelings that are similar to the results of an experienced human performing the same task. So we are not only looking for a labeling where the number of labels placed is high but also where the distribution of the placed labels is good. Our experimental results show that the new algorithm outperforms the other methods in terms of quality while still being reasonably fast and confirm that the simulated annealing method is well-suited for map labeling problems.

Leveraging Predicated Execution for Multimedia Processing

Modern compression standards such as H.264, DivX, or VC-1 provide astonishing quality at the costs of steadily increasing processing requirements. Therefore, efficient solutions for mobile multimedia devices have to effectively leverage instruction level parallelism (LLP), which is often achieved by the deployment of EPIC (explicitly parallel instruction computing) architectures. A characteristical architectural feature to increase the available ILP in the presence of control flow is predicated execution. Compilers targeting those hardware platforms are responsible to carefully convert control flow into conditional/predicated instructions - a process called if-conversion. We describe an effective if-conversion algorithm for the CHILI - a novel hardware architecture specifically designed for digital video processing and mobile multimedia consumer electronic. Several architectural characteristics such as the lack of branch prediction units, large delay slots, and the provided predication model are significantly different from previous work, typically aiming mainstream architectures such as Intel Itanium. The algorithm has been implemented for an optimizing compiler based on LLVM. Experimental results using a cycle accurate simulator for the well known benchmark suite MiBench and several multimedia codecs show a speed improvement of about 18% on average. On the same programs, our compiler achieves a speedup of 21% in comparison to the existing code generator based on gcc.

Fast native function calls for the Babel language interoperability framework

Babel is an open-source language interoperability framework tailored to the needs of high-performance scientific computing. Its primary focus is on fast in-process communication across various languages. In doing so, some additional call overhead is often inevitable. For several pairs of languages, however, shortcuts exist that allow for more efficient function calls. As Babel is a dynamic framework, the particular set of languages involved is often only known at runtime. In this work, we present a simple yet very effective optimization that can be used to reduce the call overhead between various pairs of languages. In particular, our optimization is applicable if caller and callee are implemented in the same language. We implement and evaluate these techniques for C++ and Python. When applicable, our optimization virtually eliminates the overhead for a small memory cost. Compared to previous versions of Babel, this means a speedup ranging from about 5x for simple numerical argument types up to roughly 125x for strings.