Compiler analysis for trace-level speculative multithreaded architectures

Trace-level speculative multithreaded processors exploit trace-level speculation by means of two threads working cooperatively. One thread, called the speculative thread, executes instructions ahead of the other by speculating on the result of several traces. The other thread executes speculated traces and verifies the speculation made by the first thread. In this paper, we propose a static program analysis for identifying candidate traces to be speculated. This approach identifies large regions of code whose live-output values may be successfully predicted. We present several heuristics to determine the best opportunities for dynamic speculation, based on compiler analysis and program profiling information. Simulation results show that the proposed trace recognition techniques achieve on average a speed-up close to 38% for a collection of SPEC2000 benchmarks.Peer ReviewedPostprint (published version

Thread-spawning schemes for speculative multithreading

Speculative multithreading has been recently proposed to boost performance by means of exploiting thread-level parallelism in applications difficult to parallelize. The performance of these processors heavily depends on the partitioning policy used to split the program into threads. Previous work uses heuristics to spawn speculative threads based on easily-detectable program constructs such as loops or subroutines. In this work we propose a profile-based mechanism to divide programs into threads by searching for those parts of the code that have certain features that could benefit from potential thread-level parallelism. Our profile-based spawning scheme is evaluated on a Clustered Speculative Multithreaded Processor and results show large performance benefits. When the proposed spawning scheme is compared with traditional heuristics, we outperform them by almost 20%. When a realistic value predictor and a 8-cycle thread initialization penalty is considered, the performance difference between them is maintained. The speed-up over a single thread execution is higher than 5x for a 16-thread-unit processor and close to 2x for a 4-thread-unit processor.Peer ReviewedPostprint (published version

Architecture for object-oriented programming model

Current mainstream architectures have ISAs that are not able to maintain all the information provided by the application programmer using a high level programming language. Typically, the information that is lost in compiling to a low-level ISA is related to parallelism and speculation [14]. For example some loops are typically expressed as parallel loops by the programmer but later the processor is not able to determine this level of parallelism; conditional execution might apply control independent execution that at execution time is basically impossible to detect; function and object-level parallelism is lost when code is transformed into a low-level ISA that is oblivious to programmer intentions and high-level programming structures. Object Oriented Programming Languages are arguably the most successful programming medium because they help the programmer to use well-known practices about data distribution through operations related with the associated data. Therefore object oriented models express data/execution locality more naturally and in an efficient manner. Other OO software mechanisms such as derivation and polymorphism further help the programmer to exploit locality better. Once object oriented programs have been compiled then all information about data/execution locality is completely lost in current assembly code (ISA code). Maintaining this information until runtime is crucial to improve locality and security. Finally, Object Oriented Programming Models maintain the idea of memory (data memory) far from the programmer. These are all desirable qualities that is mostly lost in the compilation to a low-level ISA that is oblivious to the Object-Oriented Programming model. This report considers implementing the Object Oriented (OO) Programming Model directly in the hardware to serve as a base to exploit object/level parallelism, speculation and heterogeneous computing. Towards this goal, we present new computer architecture that implements the OO Programming Models. All its hardware structures are objects and its Instruction Set directly utilizes objects hiding totally the notion of memory and other complex hardware structures. It also maintains all high-level programming language information until execution time. This enables efficient extraction of available parallelism in OO serial or parallel code at execution time with minimal compiler support. We will demonstrate the potential of this novel computer architecture through several examples.Postprint (published version

Twill: A Hybrid Microcontroller-FPGA Framework for Parallelizing Single- Threaded C Programs

Increasingly System-On-A-Chip platforms which incorporate both micropro- cessors and re-programmable logic are being utilized across several fields ranging from the automotive industry to network infrastructure. Unfortunately, the de- velopment tools accompanying these products leave much to be desired, requiring knowledge of both traditional embedded systems languages like C and hardware description languages like Verilog. We propose to bridge this gap with Twill, a truly automatic hybrid compiler that can take advantage of the parallelism inherent in these platforms. Twill can extract long-running threads from single threaded C code and distribute these threads across the hardware and software domains to more fully utilize the asymmetric characteristics between processors and the embedded reconfigurable logic fabric. We show that Twill provides a sig- nificant performance increase on the CHStone benchmarks with an average 1.63 times increase over the pure hardware approach and an increase of 22.2 times on average over the pure software approach while reducing the area required by the reconfigurable logic by on average 1.73 times compared to the pure hardware approach

Multiplex: Unifying Conventional and Speculative Thread-Level Parallelism on a Chip Multiprocessor

Recent proposals for Chip Multiprocessors (CMPs) advocate speculative, or implicit, threading in which the hardware employs prediction to peel off instruction sequences (i.e., implicit threads) from the sequential execution stream and speculatively executes them in parallel on multiple processor cores. These proposals augment a conventional multiprocessor, which employs explicit threading, with the ability to handle implicit threads. Current proposals focus on only implicitly-threaded code sections. This paper identifies, for the first time, the issues in combining explicit and implicit threading. We present the Multiplex architecture to combine the two threading models. Multiplex exploits the similarities between implicit and explicit threading, and provides a unified support for the two threading models without additional hardware. Multiplex groups a subset of protocol states in an implicitly-threaded CMP to provide a write-invalidate protocol for explicit threads. Using a fully-integrated compiler inf rastructure for automatic generation of Multiplex code, this paper presents a detailed performance analysis for entire benchmarks, instead of just implicitly- threaded sections, as done in previous papers. We show that neither threading models alone performs consistently better than the other across the benchmarks. A CMP with four dual-issue CPUs achieves a speedup of 1.48 and 2.17 over one dual-issue CPU, using implicit-only and explicit-only threading, respectively. Multiplex matches or outperforms the better of the two threading models for every benchmark, and a four-CPU Multiplex achieves a speedup of 2.63. Our detailed analysis indicates that the dominant overheads in an implicitly-threaded CMP are speculation state overflow due to limited L1 cache capacity, and load imbalance and data dependences in fine-grain threads

Vector coprocessor sharing techniques for multicores: performance and energy gains

Vector Processors (VPs) created the breakthroughs needed for the emergence of computational science many years ago. All commercial computing architectures on the market today contain some form of vector or SIMD processing. Many high-performance and embedded applications, often dealing with streams of data, cannot efficiently utilize dedicated vector processors for various reasons: limited percentage of sustained vector code due to substantial flow control; inherent small parallelism or the frequent involvement of operating system tasks; varying vector length across applications or within a single application; data dependencies within short sequences of instructions, a problem further exacerbated without loop unrolling or other compiler optimization techniques. Additionally, existing rigid SIMD architectures cannot tolerate efficiently dynamic application environments with many cores that may require the runtime adjustment of assigned vector resources in order to operate at desired energy/performance levels. To simultaneously alleviate these drawbacks of rigid lane-based VP architectures, while also releasing on-chip real estate for other important design choices, the first part of this research proposes three architectural contexts for the implementation of a shared vector coprocessor in multicore processors. Sharing an expensive resource among multiple cores increases the efficiency of the functional units and the overall system throughput. The second part of the dissertation regards the evaluation and characterization of the three proposed shared vector architectures from the performance and power perspectives on an FPGA (Field-Programmable Gate Array) prototype. The third part of this work introduces performance and power estimation models based on observations deduced from the experimental results. The results show the opportunity to adaptively adjust the number of vector lanes assigned to individual cores or processing threads in order to minimize various energy-performance metrics on modern vector- capable multicore processors that run applications with dynamic workloads. Therefore, the fourth part of this research focuses on the development of a fine-to-coarse grain power management technique and a relevant adaptive hardware/software infrastructure which dynamically adjusts the assigned VP resources (number of vector lanes) in order to minimize the energy consumption for applications with dynamic workloads. In order to remove the inherent limitations imposed by FPGA technologies, the fifth part of this work consists of implementing an ASIC (Application Specific Integrated Circuit) version of the shared VP towards precise performance-energy studies involving high- performance vector processing in multicore environments