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

Identifying, Quantifying, Extracting and Enhancing Implicit Parallelism

The shift of the microprocessor industry towards multicore architectures has placed a huge burden on the programmers by requiring explicit parallelization for performance. Implicit Parallelization is an alternative that could ease the burden on programmers by parallelizing applications ???under the covers??? while maintaining sequential semantics externally. This thesis develops a novel approach for thinking about parallelism, by casting the problem of parallelization in terms of instruction criticality. Using this approach, parallelism in a program region is readily identified when certain conditions about fetch-criticality are satisfied by the region. The thesis formalizes this approach by developing a criticality-driven model of task-based parallelization. The model can accurately predict the parallelism that would be exposed by potential task choices by capturing a wide set of sources of parallelism as well as costs to parallelization. The criticality-driven model enables the development of two key components for Implicit Parallelization: a task selection policy, and a bottleneck analysis tool. The task selection policy can partition a single-threaded program into tasks that will profitably execute concurrently on a multicore architecture in spite of the costs associated with enforcing data-dependences and with task-related actions. The bottleneck analysis tool gives feedback to the programmers about data-dependences that limit parallelism. In particular, there are several ???accidental dependences??? that can be easily removed with large improvements in parallelism. These tools combine into a systematic methodology for performance tuning in Implicit Parallelization. Finally, armed with the criticality-driven model, the thesis revisits several architectural design decisions, and finds several encouraging ways forward to increase the scope of Implicit Parallelization.unpublishednot peer reviewe

Code transformations based on speculative SDC scheduling

Code motion and speculations are usually exploited in the High Level Synthesis of control dominated applications to improve the performances of the synthesized designs. Selecting the transformations to be applied is not a trivial task: their effects can indeed indirectly spread across the whole design, potentially worsening the quality of the results. In this paper we propose a code transformation flow, based on a new extension of the System of Difference Constraints (SDC) scheduling algorithm, which introduces a large number of transformations, whose profitability is guaranteed by SDC formulation. Experimental results show that the proposed technique in average reduces the execution time of control dominated applications by 37% with respect to a commercial tool without increasing the area usage

Beyond Reuse Distance Analysis: Dynamic Analysis for Characterization of Data Locality Potential

Emerging computer architectures will feature drastically decreased flops/byte (ratio of peak processing rate to memory bandwidth) as highlighted by recent studies on Exascale architectural trends. Further, flops are getting cheaper while the energy cost of data movement is increasingly dominant. The understanding and characterization of data locality properties of computations is critical in order to guide efforts to enhance data locality. Reuse distance analysis of memory address traces is a valuable tool to perform data locality characterization of programs. A single reuse distance analysis can be used to estimate the number of cache misses in a fully associative LRU cache of any size, thereby providing estimates on the minimum bandwidth requirements at different levels of the memory hierarchy to avoid being bandwidth bound. However, such an analysis only holds for the particular execution order that produced the trace. It cannot estimate potential improvement in data locality through dependence preserving transformations that change the execution schedule of the operations in the computation. In this article, we develop a novel dynamic analysis approach to characterize the inherent locality properties of a computation and thereby assess the potential for data locality enhancement via dependence preserving transformations. The execution trace of a code is analyzed to extract a computational directed acyclic graph (CDAG) of the data dependences. The CDAG is then partitioned into convex subsets, and the convex partitioning is used to reorder the operations in the execution trace to enhance data locality. The approach enables us to go beyond reuse distance analysis of a single specific order of execution of the operations of a computation in characterization of its data locality properties. It can serve a valuable role in identifying promising code regions for manual transformation, as well as assessing the effectiveness of compiler transformations for data locality enhancement. We demonstrate the effectiveness of the approach using a number of benchmarks, including case studies where the potential shown by the analysis is exploited to achieve lower data movement costs and better performance.Comment: Transaction on Architecture and Code Optimization (2014

Distributed data cache designs for clustered VLIW processors

Wire delays are a major concern for current and forthcoming processors. One approach to deal with this problem is to divide the processor into semi-independent units referred to as clusters. A cluster usually consists of a local register file and a subset of the functional units, while the L1 data cache typically remains centralized in What we call partially distributed architectures. However, as technology evolves, the relative latency of such a centralized cache will increase, leading to an important impact on performance. In this paper, we propose partitioning the L1 data cache among clusters for clustered VLIW processors. We refer to this kind of design as fully distributed processors. In particular; we propose and evaluate three different configurations: a snoop-based cache coherence scheme, a word-interleaved cache, and flexible LO-buffers managed by the compiler. For each alternative, instruction scheduling techniques targeted to cyclic code are developed. Results for the Mediabench suite'show that the performance of such fully distributed architectures is always better than the performance of a partially distributed one with the same amount of resources. In addition, the key aspects of each fully distributed configuration are explored.Peer ReviewedPostprint (published version