Abstract. Thread-level speculation (TLS) allows potentially dependent threads to speculatively execute in parallel, thus making it easier for the compiler to extract parallel threads. However, the high cost associated with unbalanced load, failed speculation, and inter-thread value communication makes it difficult to obtain the desired performance unless the speculative threads are carefully chosen.In this paper, we focus on extracting parallel threads from loops in generalpurpose applications because loops, with their regular structures and significant coverage on execution time, are ideal candidates for extracting parallel threads. General-purpose applications, however, usually contain a large number of nested loops with unpredictable parallel performance and dynamic behavior, thus making it difficult to decide which set of loops should be parallelized to improve overall program performance. Our proposed loop selection algorithm addresses all these difficulties. We have found that (i) with the aid of profiling information, compiler analyses can achieve a reasonably accurate estimation of the performance of parallel execution, and that (ii) different invocations of a loop may behave differently, and exploiting this dynamic behavior can further improve performance. With a judicious choice of loops, we can improve the overall program performance of SPEC2000 integer benchmarks by as much as 20%.

Abstract. Data dependence analysis is the foundation to many reordering related compiler optimizations and loop parallelization. Traditional data dependence analysis algorithms are developed primarily for Fortran-like subscripted array variables. They are not very effective for pointer-based references in C or C++. With more advanced hardware support for speculative execution, such as the advanced load instructions in Intel's IA64 architecture, some data dependences with low probability can be speculatively ignored. However, such speculative optimizations must be carefully applied to avoid excessive cost associated with potential mis-speculations. Data dependence profiling is one way to provide probabilistic information on data dependences to guide such speculative optimizations and speculative thread generation.

This paper studies and compares the use of data prefetching and an alternative mechanism, data forwarding, for reducing memory latency due to interprocessor communication in cache coherent, shared memory multiprocessors. Two multiprocessor prefetching algorithms are presented and compared. A simple blocked vector prefetching algorithm, considerably less complex than existing software pipelined prefetching algorithms, is shown to be effective in reducing memory latency and increasing performance. A Forwarding Write operation is used to evaluate the effectiveness of forwarding. The use of data forwarding results in significant performance improvements over data prefetching for codes exhibiting less spatial locality. Algorithms for data prefetching and data forwarding are implemented in a parallelizing compiler. Evaluation of the proposed schemes and algorithms is accomplished via execution-driven simulation of large, optimized, parallel numerical application codes with loop-level and vector parallelism. More data, discussion, and experiment details can be found in [1].

Abstract-Research on compiler techniques for thread-level loop speculation has so far remained on studying its performance limits: loop candidates that are worthy of parallelization are manually selected by the researchers or based on extensive profiling and pre-execution. It is therefore difficult to include them in a production compiler for speculative multithreaded multicore processors. In a way, existing techniques are statically adaptive ("realized" by the researchers for different inputs) yet dynamically greedy (since all iterations of all selected loop candidates are always parallelized at run time). This paper introduces a SEED (Statically GrEEdy and Dynamically Adaptive) approach for thread-level speculation on loops that is quite different from most other existing techniques. SEED relies on the compiler to select and optimize loop candidates greedily (possibly in an input-independent way) and provides a runtime scheduler to schedule loop iterations adaptively. To select loops for parallelization at run time (subject to program inputs), loop iterations are prioritized in terms of their potential benefits rather than their degree of speculation as in many prior studies. In our current implementation, the benefits of speculative threads are estimated by a simple yet effective cost model. It comprises a mechanism for efficiently tracing the loop nesting structures of the program and a mechanism for predicting the outcome of speculative threads. We have evaluated SEED using a set of SPECint2000 and Olden benchmarks. Compared to existing techniques with a program's loop candidates being ideally selected a priori, SEED can achieve comparable or better performance while aututomating the entire loop candidate selection process.

Speculative execution, such as control speculation and data speculation, is an effective way to improve program performance. Using edge/path profile information or simple heuristic rules, existing compiler frameworks can adequately incorporate and exploit control speculation. However, very little has been done so far to allow existing compiler frameworks to incorporate and exploit data speculation effectively in various program transformations beyond instruction scheduling. This paper proposes a speculative SSA form to incorporate information from alias profiling and/or heuristic rules for data speculation, thus allowing existing program analysis frameworks to be easily extended to support both control and data speculation. Such a general framework is very useful for EPIC architectures that provide checking (such as advanced load address table (ALAT) [10]) on data speculation to guarantee the correctness of program execution. We use SSAPRE [21] as one example to illustrate how to incorporate data speculation in those important compiler optimizations such as partial redundancy elimination (PRE), register promotion, strength reduction and linear function test replacement. Our extended framework allows both control and data speculation to be performed on top of SSAPRE and, thus, enables more aggressive speculative optimizations. The proposed framework has been implemented on Intel's Open Research Compiler (ORC). We present experimental data on some SPEC2000 benchmark programs to demonstrate the usefulness of this framework and how data speculation benefits partial redundancy elimination.

Abstract-The computer industry has adopted multi-threaded and multicore architectures as the clock rate increase stalled in early 2000's. It was hoped that the continuous improvement of single-program performance could be achieved through these architectures. However, traditional parallelizing compilers often fail to effectively parallelize general-purpose applications which typically have complex control flow and excessive pointer usage. Recently hardware techniques such as Transactional Memory (TM) and ThreadLevel Speculation (TLS) have been proposed to simplify the task of parallelization by using speculative threads. Potential of speculative parallelism in general-purpose applications like SPEC CPU 2000 have been well studied and shown to be moderately successful. Preliminary work examining the potential parallelism in SPEC2006 deployed parallel threads with a restrictive TLS execution model and limited compiler support, and thus only showed limited performance potential. In this paper, we first analyze the cross-iteration dependence behavior of SPEC 2006 benchmarks and show that more parallelism potential is available in SPEC 2006 benchmarks, comparing to SPEC2000. We further use a state-of-the-art profile-driven TLS compiler to identify loops that can be speculatively parallelized. Overall, we found that with optimal loop selection we can potentially achieve an average speedup of 60% on four cores over what could be achieved by a traditional parallelizing compiler such as Intel's ICC compiler. We also found that an additional 11% improvement can be potentially obtained on selected benchmarks using 8 cores when we extend TLS on multiple loop levels as opposed to restricting to a single loop level.

Recent micro-architectural research has proposed various schemes to enhance processors with additional tags to track various properties of a program. Such a technique, which is usually referred to as information flow tracking, has been widely applied to secure software execution (e.g., taint tracking), protect software privacy and improve performance (e.g., control speculation).In this paper, we propose a novel use of information flow tracking to obfuscate the whole control flow of a program with only modest performance degradation, to defeat malicious code injection, discourage software piracy and impede malware analysis. Specifically, we exploit two common features in information flow tracking: the architectural support for automatic propagation of tags and violation handling of tag misuses. Unlike other schemes that use tags as oracles to catch attacks (e.g., taint tracking) or speculation failures, we use the tags as flow-sensitive predicates to hide normal control flow transfers: the tags are used as predicates for control flow transfers to the violation handler, where the real control flow transfer happens.We have implemented a working prototype based on Itanium processors, by leveraging the hardware support for control speculation. Experimental results show that BOSH can obfuscate the whole control flow with only a mean of 26.7% (ranging from 4% to 59%) overhead on SPECINT2006. The increase in code size and compilation time is also modest.

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