Dynamic Compilation System Research Articles

Pattern-Based Dynamic Compilation System for CGRAs With Online Configuration Transformation

Prevailing data-intensive applications, such as artificial intelligence and internet of things, demand considerable compute capability. Coarse-grained reconfigurable architectures (CGRAs) can meet this demand via providing abundant compute resources. However, compilation has become an essential problem because the increasing resources need to be orchestrated efficiently. Static compilation is insufficient due to conservative resource allocation and exponentially increasing time cost while state-of-the-art dynamic compilation still performs poorly in both generality and efficiency. This article proposes a dynamic compilation system for CGRAs through online pattern-based configuration transformation, which enables virtualization to improve resource utilization and flexibility. It utilizes statically-generated patterns to straightforwardly determine dynamic placement of registers and operations so that the transformation algorithm has a low complexity. Domain-specific features are extracted by a k-means clustering algorithm to help improve the quality of patterns. The experimental results show that statically compiled applications can be transformed onto arbitrary resources at runtime, reserving 73.5 (22.8-163.3 percent) of the original performance/resource on average, 9.1 (0-52.9 percent) better than the state-of-theart non-general methods.

Smart selection of optimizations in dynamic compilers

SummaryDynamic compilers perform compilation and generation of target code during runtime, implying that the compilation time is added into the program runtime. Thus, to build a high‐performing dynamic compilation system, it is crucial to be able to generate high‐quality code and, at the same time, have a small compilation cost. In this article, we present an approach that uses machine learning to select sequences of optimization for dynamic compilation that considers both code quality and compilation overhead. Our approach starts by training a model, offline, with a knowledge bank of those sequences with low overhead and high‐quality code generation capability using a genetic heuristic. Then, this bank is used to guide the smart selection of optimizations sequences for the compilation of code fragments during the emulation of an application. We evaluate the proposed strategy in two LLVM‐based dynamic binary translators, namely OI‐DBT and HQEMU, and show that these two translators can achieve average speedups of 1.26x and 1.15x in MiBench and Spec Cpu benchmarks, respectively.

TRICK

Compilers, during compilation, analyze the application being compiled and build up extensive knowledge about the program. This knowledge is essential for the compiler to produce correct object code. Though some part of this knowledge is retained in the generated object files as symbol table information to be used by the linker and/or debugger, most of it is discarded after the compilation is done. In this paper, we introduce the TRICK framework, which is an attempt to retain and reuse this internal information generated by the compiler as part of its program analysis, in building new tools or enhancing existing tools as well for reuse by the compiler for continuous program optimization. We present examples of how development and maintenance of various program analysis tools can be simplified by using the TRICK framework describing tools developed by our group as well as how TRICK framework can be employed in continuous program optimization by the compiler. TRICK framework can be part of both static and dynamic compilation system, though our current usage model is in the context of a static compilation system,

Design and evaluation of dynamic optimizations for a Java just-in-time compiler

The high performance implementation of Java Virtual Machines (JVM) and Just-In-Time (JIT) compilers is directed toward employing a dynamic compilation system on the basis of online runtime profile information. The trade-off between the compilation overhead and performance benefit is a crucial issue for such a system. This article describes the design and implementation of a dynamic optimization framework in a production-level Java JIT compiler, together with two techniques for profile-directed optimizations: method inlining and code specialization. Our approach is to employ a mixed mode interpreter and a three-level optimizing compiler, supporting level-1 to level-3 optimizations, each of which has a different set of trade-offs between compilation overhead and execution speed. A lightweight sampling profiler operates continuously during the entire period while applications are running to monitor the programs' hot spots. Detailed information on runtime behavior can be collected by dynamically generating instrumentation code that is installed to and uninstalled from the specified recompilation target code. Value profiling with this instrumentation mechanism allows fully automatic profile-directed method inlining and code specialization to be performed on the basis of call site information or specific parameter values at the higher optimization levels. The experimental results show that our approach offers high performance and low compilation overhead in both program startup and steady state measurements in comparison to the previous systems. The two profile-directed optimization techniques contribute significant portions of the improvements.

A retrospective on

Previous selective dynamic compilation systems have demonstrated that dynamic compilation can achieve performance improvements at low cost on small kernels, but they have had difficulty scaling to larger programs. To overcome this limitation, we developed DyC, a selective dynamic compilation system that includes more sophisticated and flexible analyses and transformations. DyC is able to achieve good performance improvements on programs that are much larger and more complex than the kernels. We analyze the individual optimizations of DyC and assess their impact on performance collectively and individually.

A region-based compilation technique for a Java just-in-time compiler

Method inlining and data flow analysis are two major optimization components for effective program transformations, however they often suffer from the existence of rarely or never executed code contained in the target method. One major problem lies in the assumption that the compilation unit is partitioned at method boundaries. This paper describes the design and implementation of a region-based compilation technique in our dynamic compilation system, in which the compiled regions are selected as code portions without rarely executed code. The key part of this technique is the region selection, partial inlining, and region exit handling. For region selection, we employ both static heuristics and dynamic profiles to identify rare sections of code. The region selection process and method inlining decision are interwoven, so that method inlining exposes other targets for region selection, while the region selection in the inline target conserves the inlining budget, leading to more method inlining. Thus the inlining process can be performed for parts of a method, not for the entire body of the method. When the program attempts to exit from a region boundary, we trigger recompilation and then rely on on-stack replacement to continue the execution from the corresponding entry point in the recompiled code. We have implemented these techniques in our Java JIT compiler, and conducted a comprehensive evaluation. The experimental results show that the approach of region-based compilation achieves approximately 5% performance improvement on average, while reducing the compilation overhead by 20 to 30%, in comparison to the traditional function-based compilation techniques.

Towards automatic construction of staged compilers

Some compilation systems, such as offline partial evaluators and selective dynamic compilation systems, support staged optimizations. A staged optimization is one where a logically single optimization is broken up into stages, with the early stage(s) performing preplanning set-up work, given any available partial knowledge about the program to be compiled, and the final stage completing the optimization. The final stage can be much faster than the original optimization by having much of its work performed by the early stages. A key limitation of current staged optimizers is that they are written by hand, sometimes in an ad hoc manner. We have developed a framework called the Staged Compilation Framework (SCF) for systematically and automatically converting single-stage optimizations into staged versions. The framework is based on a combination of aggressive partial evaluation and dead-assignment elimination. We have implemented SCF in Standard ML. A preliminary evaluation shows that SCF can speed up classical optimization of some commonly used C functions by up to 12× (and typically between 4.5× and 5.5×).

Partial method compilation using dynamic profile information

The traditional tradeoff when performing dynamic compilation is that of fast compilation time versus fast code performance. Most dynamic compilation systems for Java perform selective compilation and/or optimization at a method granularity. This is the not the optimal granularity level. However, compiling at a sub-method granularity is thought to be too complicated to be practical. This paper describes a straightforward technique for performing compilation and optimizations at a finer, sub-method granularity. We utilize dynamic profile data to determine intra-method code regions that are rarely or never executed, and compile and optimize the code without those regions. If a branch that was predicted to be rare is actually taken at run time, we fall back to the interpreter or dynamically compile another version of the code. By avoiding compiling and optimizing code that is rarely executed, we are able to decrease compile time significantly, with little to no degradation in performance. Futhermore, ignoring rarely-executed code can open up more optimization opportunities on the common paths. We present two optimizations---partial dead code elimination and rare-path-sensitive pointer and escape analysis---that take advantage of rare path information. Using these optimizations, our technique is able to improve performance beyond the compile time improvements

The benefits and costs of DyC's run-time optimizations

DyC selectively dynamically compiles programs during their execution, utilizing the run-time-computed values of variables and data structures to apply optimizations that are based on partial evaluation. The dynamic optimizations are preplanned at static compile time in order to reduce their run-time cost; we call this staging . DyC's staged optimizations include (1) an advanced binding-time analysis that supports polyvariant specialization (enabling both single-way and multiway complete loop unrolling), polyvariant division, static loads, and static calls, (2) low-cost, dynamic versions of traditional global optimizations, such as zero and copy propagation and dead-assignment elimination, and (3) dynamic peephole optimizations, such as strength reduction. Because of this large suite of optimizations and its low dynamic compilation overhead, DyC achieves good performance improvements on programs that are larger and more complex than the kernels previously targeted by other dynamic compilation systems. This paper evaluates the benefits and costs of applying DyC's optimizations. We assess their impact on the performance of a variety of small to medium-sized programs, both for the regions of code that are actually transformed and for the entire application as a whole. Our study includes an analysis of the contribution to performance of individual optimizations, the performance effect of changing the applications' inputs, and a detailed accounting of dynamic compilation costs.

Linear scan register allocation

We describe a new algorithm for fast global register allocation called linear scan . This algorithm is not based on graph coloring, but allocates registers to variables in a single linear-time scan of the variables' live ranges. The linear scan algorithm is considerably faster than algorithms based on graph coloring, is simple to implement, and results in code that is almost as efficient as that obtained using more complex and time-consuming register allocators based on graph coloring. The algorithm is of interest in applications where compile time is a concern, such as dynamic compilation systems, “just-in-time” compilers, and interactive development environments.

An evaluation of staged run-time optimizations in DyC

Previous selective dynamic compilation systems have demonstrated that dynamic compilation can achieve performance improvements at low cost on small kernels, but they have had difficulty scaling to larger programs. To overcome this limitation, we developed DyC, a selective dynamic compilation system that includes more sophisticated and flexible analyses and transformations. DyC is able to achieve good performance improvements on programs that are much larger and more complex than the kernels. We analyze the individual optimizations of DyC and assess their impact on performance collectively and individually.

Annotation-directed run-time specialization in C

We present the design of a dynamic compilation system for C. Directed by a few declarative user annotations specifying where and on what dynamic compilation is to take place, a binding time analysis computes the set of run-time constants at each program point in each annotated procedure's control flow graph; the analysis supports program-point-specific polyvariant division and specialization. The analysis results guide the construction of a specialized run-time specializer for each dynamically compiled region; the specializer supports various caching strategies for managing dynamically generated code and supports mixes of speculative and demand-driven specialization of dynamic branch successors. Most of the key cost/benefit trade-offs in the binding time analysis and the run-time specialize are open to user control through declarative policy annotations. Our design is being implemented in the context of art existing optimizing compiler.

Tcc

tcc is a compiler that provides efficient and high-level access to dynamic code generation. It implements the 'C ("Tick-C") programming language, an extension of ANSI C that supports dynamic code generation [15]. 'C gives power and flexibility in specifying dynamically generated code: whereas most other systems use annotations to denote run-time invariants. 'C allows the programmer to specify and compose arbitrary expressions and statements at run time. This degree of control is needed to efficiently implement some of the most important applications of dynamic code generation, such as "just in time" compilers [17] and efficient simulators [10, 48, 46].The paper focuses on the techniques that allow tcc to provide 'C's flexibility and expressiveness without sacrificing run-time code generation efficiency. These techniques include fast register allocation, efficient creation and composition of dynamic code specifications, and link-time analysis to reduce the size of dynamic code generators. tcc also implements two different dynamic code generation strategies, designed to address the tradeoff of dynamic compilation speed versus generated code quality. To characterize the effects of dynamic compilation, we present performance measurements for eleven programs compiled using tcc. On these applications, we measured performance improvements of up to one order of magnitude.To encourage further experimentation and use of dynamic code generation, we are making the tcc compiler available in the public domain. This is, to our knowledge, the first high-level dynamic compilation system to be made available.