Efficient optimization of memory accesses in parallel programs

The power, frequency, and memory wall problems have caused a major shift in mainstream computing by introducing processors that contain multiple low power cores. As multi-core processors are becoming ubiquitous, software trends in both parallel programming languages and dynamic compilation have added new challenges to program compilation for multi-core processors. This thesis proposes a combination of high-level and low-level compiler optimizations to address these challenges. The high-level optimizations introduced in this thesis include new approaches to May-Happen-in-Parallel analysis and Side-Effect analysis for parallel programs and a novel parallelism-aware Scalar Replacement for Load Elimination transformation. A new Isolation Consistency (IC) memory model is described that permits several scalar replacement transformation opportunities compared to many existing memory models. The low-level optimizations include a novel approach to register allocation that retains the compile time and space efficiency of Linear Scan, while delivering runtime performance superior to both Linear Scan and Graph Coloring. The allocation phase is modeled as an optimization problem on a Bipartite Liveness Graph (BLG) data structure. The assignment phase focuses on reducing the number of spill instructions by using register-to-register move and exchange instructions wherever possible. Experimental evaluations of our scalar replacement for load elimination transformation in the Jikes RVM dynamic compiler show decreases in dynamic counts for getfield operations of up to 99.99%, and performance improvements of up to 1.76x on 1 core, and 1.39x on 16 cores, when compared with the load elimination algorithm available in Jikes RVM. A prototype implementation of our BLG register allocator in Jikes RVM demonstrates runtime performance improvements of up to 3.52x relative to Linear Scan on an x86 processor. When compared to Graph Coloring register allocator in the GCC compiler framework, our allocator resulted in an execution time improvement of up to 5.8%, with an average improvement of 2.3% on a POWER5 processor. With the experimental evaluations combined with the foundations presented in this thesis, we believe that the proposed high-level and low-level optimizations are useful in addressing some of the new challenges emerging in the optimization of parallel programs for multi-core architectures

Extended Linear Scan: an Alternate Foundation for Global Register Allocation

Abstract. In this paper, we extend past work on Linear Scan register allocation, and propose two Extended Linear Scan (ELS) algorithms that retain the compiletime efficiency of past Linear Scan algorithms while delivering performance that can match or surpass that of Graph Coloring. Specifically, this paper makes the following contributions: – We highlight three fundamental theoretical limitations in using Graph Coloring as a foundation for global register allocation, and introduce a basic Extended Linear Scan algorithm, ELS0, which addresses all three limitations for the problem of Spill-Free Register Allocation. – We introduce the ELS1 algorithm which extends ELS0 to obtain a greedy algorithm for the problem of Register Allocation with Total Spills. – Finally, we present experimental results to compare the Graph Coloring and Extended Linear Scan algorithms. Our results show that the compile-time speedups for ELS1 relative to GC were significant, and varied from 15 × to 68×. In addition, the resulting execution time improved by up to 5.8%, with an average improvement of 2.3%. Together, these results show that Extended Linear Scan is promising as an alternate foundation for global register allocation, compared to Graph Coloring, due to its compile-time scalability without loss of execution time performance.

Interprocedural Load Elimination for Dynamic Optimization of Parallel Programs

Abstract—Load elimination is a classical compiler transfor-mation that is increasing in importance for multi-core and many-core architectures. The effect of the transformation is to replace a memory access, such as a read of an object field or an array element, by a read of a compiler-generated temporary that can be allocated in faster and more energy-efficient storage structures such as registers and local memories (scratchpads). Unfortunately, current just-in-time and dynamic compilers perform load elimination only in limited situations. In particular, they usually make worst-case assumptions about potential side effects arising from parallel constructs and method calls. These two constraints interact with each other since parallel constructs are usually translated to low-level runtime library calls. In this paper, we introduce an interprocedural load elimina-tion algorithm suitable for use in dynamic optimization of par-allel programs. The main contributions of the paper include: a) an algorithm for load elimination in the presence of three core parallel constructs – async, finish, and isolated, b) efficient side-effect analysis for method calls, c) extended side-effect analysis for parallel constructs using an Isolation Consistency memory model, and d) performance results to study the impact of load elimination on a set of standard benchmarks using an implementation of the algorithm in Jikes RVM for optimizing programs written in a subset of the X10 v1.5 language. Our performance results show decreases in dynamic counts for getfield operations of up to 99.99%, and performance improvements of up to 1.76 × on 1 core, and 1.39 × on 16 cores, when comparing the algorithm in this paper with the load elimination algorithm available in Jikes RVM. Keywords-Load elimination; scalar replacement; parallel program; dynamic compilation; dynamic optimization; mem-ory model. I

Register Allocation using Bipartite Liveness Graphs

Register allocation is an essential optimization for all compilers. A number of sophisticated register allocation algorithms have been developed based on Graph Coloring (GC) over the years. However, these algorithms pose three major limitations in practice. First, construction of a full interference graph can be a major source of space and time overhead in the compiler. Second, the interference graph lacks information on the program points at which two variables may interfere. Third, integration of coloring and coalescing leads to a coupling between register allocation and register assignment, which can further compromise the effectiveness of the solution. This paper addresses these limitations by making a clean separation between the register allocation and register assignment phases. Allocation is modeled as an optimization problem on a new data structure called the Bipartite Liveness Graph (BLG). We model the register assignment phase as a separate optimization problem that avoids some spill instructions by generating register-to-register moves and exchange instructions and at the same time, performs move coalescing and handles register class constraints. We implemented our BLG allocator in both the LLVM static compiler infrastructure and the Jikes RVM dynamic compiler infrastructure. In the LLVM evaluation, our BLG register allocator results in a performance improvement of up to 7.8% for SpecCPU 2006 benchmarks and a significantly lower compile-time overhead compared to a Chaitin-Briggs GC register allocator with both allocators using the same spill-code generator. The BLG allocator delivers performance comparable to the existing LLVM 2.7 Linear Scan register allocator that includes additional optimizations such as live-range splitting and backtracking techniques that are currently not present in the BLG allocator. In the Jikes RVM evaluation, the BLG register allocator delivers runtime performance improvements of up to 30.7% for Java Grande Forum benchmarks and up to 9% for Dacapo benchmarks relative to Linear Scan (LS) with a modest increase in compile-time

Interprocedural Strength Reduction of Critical Sections in Explicitly-Parallel Programs

In this paper, we introduce novel compiler optimization techniques to reduce the number of operations performed in critical sections that occur in explicitly-parallel programs. Specifically, we focus on three code transformations: 1) Partial Strength Reduction (PSR) of critical sections to replace critical sections by non-critical sections on certain control flow paths; 2) Critical Load Elimination (CLE) to replace memory accesses within a critical section by accesses to scalar temporaries that contain values loaded outside the critical section; and 3) Non-critical Code Motion (NCM) to hoist thread-local computations out of critical sections. The effectiveness of the first two transformations is further increased by inter-procedural analysis. The effectiveness of our techniques has been demonstrated for critical section constructs from three different explicitly-parallel programming models — the isolated construct in Habanero Java (HJ), the synchronized construct in standard Java, and transactions in the Java-based Deuce software transactional memory system. We used two SMP platforms (a 16-core Intel Xeon SMP and a 32-Core IBM Power7 SMP) to evaluate our optimizations on 17 explicitly-parallel benchmark programs that span all three models. Our results show that the optimizations introduced in this paper can deliver measurable performance improvements that increase in magnitude when the program is run with a larger number of processor cores. These results underscore the importance of optimizing critical sections, and the fact that the benefits from such optimizations will continue to increase with increasing numbers of cores in future many-core processors

Efficient Selection of Vector Instructions using Dynamic Programming

Accelerating program performance via SIMD vector units is very common in modern processors, as evidenced by the use of SSE, MMX, VSE, and VSX SIMD instructions in multimedia, scientific, and embedded applications. To take full advantage of the vector capabilities, a compiler needs to generate efficient vector code automatically. However, most commercial and open-source compilers fall short of using the full potential of vector units, and only generate vector code for simple innermost loops. In this paper, we present the design and implementation of an auto-vectorization framework in the backend of a dynamic compiler that not only generates optimized vector code but is also well integrated with the instruction scheduler and register allocator. The framework includes a novel compile-time efficient dynamic programming-based vector instruction selection algorithm for straight-line code that expands opportunities for vectorization in the following ways: (1) scalar packing explores opportunities of packing multiple scalar variables into short vectors; (2) judicious use of shuffle and horizontal vector operations, when possible; and (3) algebraic reassociation expands opportunities for vectorization by algebraic simplification. We report performance results on the impact of auto-vectorization on a set of standard numerical benchmarks using the Jikes RVM dynamic compilation environment. Our results show performance improvement of up to 57.71% on an Intel Xeon processor, compared to non-vectorized execution, with a modest increase in compile time in the range from 0.87% to 9.992%. An investigation of the SIMD parallelization performed by v11.1 of the Intel Fortran Compiler (IFC) on three benchmarks shows that our system achieves speedup with vectorization in all three cases and IFC does not. Finally, a comparison of our approach with an implementation of the Superword Level Parallelization (SLP) algorithm from [21], shows that our approach yields a performance improvement of up to 13.78% relative to SLP

Concurrent Programming—Parallel Programming General Terms Languages, Verification

X10 is a modern object-oriented programming language designed for high performance, high productivity programming of parallel and multi-core computer systems. Compared to the lower-level thread-based concurrency model in the Java TM language, X10 has higher-level concurrency constructs such as async, atomic and finish built into the language to simplify creation, analysis and optimization of parallel programs. In this paper, we introduce a new algorithm for May-Happen-in-Parallel (MHP)analysisofX10 programs. The analysis algorithm is based on simple path traversals in the Program Structure Tree, and does not rely on pointer alias analysis of thread objects as in MHP analysis for Java programs. We introduce a more precise definition of the MHP relation than in past work by adding condition vectors that identify execution instances for which the MHP relation holds, instead of just returning a single true/false value for all pairs of executing instances. Further, MHP analysis is refined in our approach by using the observation that two statement instances which occur in atomic sections that execute at the same X10 place must have MHP = false. We expect that our MHP analysis algorithm will be applicable to any language that adopts the core concepts of places, async, finish, and atomic sections from the X10 programming model. We also believe that this approach offers the best of two worlds to programmers and parallel programming tools — higher-level abstractions of concurrency coupled with simple and efficient analysis algorithms

Work-First and Help-First Scheduling Policies for Terminally Strict Parallel Programs

Multiple programming models are emerging to address an increased need for dynamic task parallelism in applications for multicore processors and shared-address space parallel computing. Examples include OpenMP 3.0, Java Concurrency Utilities, Microsoft Task Parallel Library, Intel Thread Building Blocks, Cilk, X10, Chapel, and Fortress. Scheduling algorithms based on work stealing, as embodied in Cilk’s implementation of dynamic spawn-sync parallelism, are gaining in popularity but also have inherent limitations. In this paper, we address the problem of efficient and scalable implementation of X10’s terminally strict async-finish task parallelism, which is more general than Cilk’s fully strict spawn-sync parallelism. We introduce a new work-stealing scheduler with compiler support for async-finish task parallelism that can accommodate both work-first and help-first scheduling policies. Performance results on two different multicore SMP platforms show significant improvements due to our new work-stealing algorithm compared to the existing work-sharing scheduler for X10, and also provide insight on scenarios in which the help-first policy yields better results than the work-first policy and vice versa

Optimal bitwise register allocation using integer linear programming

Abstract. This paper addresses the problem of optimal global register allocation. The register allocation problem is expressed as an integer linear programming problem and solved optimally. The model is more flexible than previous graphcoloring based methods and thus allows for register allocations with significantly fewer moves and spills. The formulation can also model complex architectural features, such as bit-wise access to registers. With bit-wise access to registers, multiple subword temporaries can be stored in a single register and accessed efficiently, resulting in a register allocation problem that cannot be addressed effectively with simple graph coloring. The paper describes techniques that can help reduce the problem size of the ILP formulation, making the algorithm feasible in practice. Preliminary empirical results from an implementation prototype are reported.