Energy-Efficient Work-Stealing Language Runtimes

Work stealing is a promising approach to constructing multithreaded program runtimes of parallel programming languages. This paper presents HERMES, an energy-efficient work-stealing language runtime. The key insight is that threads in a work-stealing environment – thieves and victims – have varying impacts on the overall program running time, and a coordination of their execution “tempo ” can lead to energy efficiency with minimal performance loss. The centerpiece of HERMES is two complementary algorithms to coordinate thread tempo: the workpath-sensitive algorithm determines tempo for each thread based on thief-victim relationships on the execution path, whereas the workload-sensitive algorithm selects appropriate tempo based on the size of work-stealing deques. We construct HERMES on top of Intel Cilk Plus’s runtime, and implement tempo adjustment through standard Dynamic Voltage and Frequency Scaling (DVFS). Benchmarks running on HERMES demonstrate an average of 11-12 % energy savings with an average of 3-4% performance loss through meter-based measurements over commercial CPUs. 1

TaskGenX: A Hardware-Software Proposal for Accelerating Task Parallelism

As chip multi-processors (CMPs) are becoming more and more complex, software solutions such as parallel programming models are attracting a lot of attention. Task-based parallel programming models offer an appealing approach to utilize complex CMPs. However, the increasing number of cores on modern CMPs is pushing research towards the use of fine grained parallelism. Task-based programming models need to be able to handle such workloads and offer performance and scalability. Using specialized hardware for boosting performance of task-based programming models is a common practice in the research community. Our paper makes the observation that task creation becomes a bottleneck when we execute fine grained parallel applications with many task-based programming models. As the number of cores increases the time spent generating the tasks of the application is becoming more critical to the entire execution. To overcome this issue, we propose TaskGenX. TaskGenX offers a solution for minimizing task creation overheads and relies both on the runtime system and a dedicated hardware. On the runtime system side, TaskGenX decouples the task creation from the other runtime activities. It then transfers this part of the runtime to a specialized hardware. We draw the requirements for this hardware in order to boost execution of highly parallel applications. From our evaluation using 11 parallel workloads on both symmetric and asymmetric multicore systems, we obtain performance improvements up to 15×, averaging to 3.1× over the baseline.This work has been supported by the RoMoL ERC Advanced Grant (GA 321253), by the European HiPEAC Network of Excellence, by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P), by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 671697 and No. 779877. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal fellowship number RYC-2016-21104. Finally, the authors would like to thank Thomas Grass for his valuable help with the simulator.Peer ReviewedPostprint (author's final draft

The cooperative parallel: A discussion about run-time schedulers for nested parallelism

Nested parallelism is a well-known parallelization strategy to exploit irregular parallelism in HPC applications. This strategy also fits in critical real-time embedded systems, composed of a set of concurrent functionalities. In this case, nested parallelism can be used to further exploit the parallelism of each functionality. However, current run-time implementations of nested parallelism can produce inefficiencies and load imbalance. Moreover, in critical real-time embedded systems, it may lead to incorrect executions due to, for instance, a work non-conserving scheduler. In both cases, the reason is that the teams of OpenMP threads are a black-box for the scheduler, i.e., the scheduler that assigns OpenMP threads and tasks to the set of available computing resources is agnostic to the internal execution of each team. This paper proposes a new run-time scheduler that considers dynamic information of the OpenMP threads and tasks running within several concurrent teams, i.e., concurrent parallel regions. This information may include the existence of OpenMP threads waiting in a barrier and the priority of tasks ready to execute. By making the concurrent parallel regions to cooperate, the shared computing resources can be better controlled and a work conserving and priority driven scheduler can be guaranteed.Peer ReviewedPostprint (author's final draft

elPrep: high-performance preparation of sequence alignment/map files for variant calling

elPrep is a high-performance tool for preparing sequence alignment/map files for variant calling in sequencing pipelines. It can be used as a replacement for SAMtools and Picard for preparation steps such as filtering, sorting, marking duplicates, reordering contigs, and so on, while producing identical results. What sets elPrep apart is its software architecture that allows executing preparation pipelines by making only a single pass through the data, no matter how many preparation steps are used in the pipeline. elPrep is designed as a multithreaded application that runs entirely in memory, avoids repeated file I/O, and merges the computation of several preparation steps to significantly speed up the execution time. For example, for a preparation pipeline of five steps on a whole-exome BAM file (NA12878), we reduce the execution time from about 1: 40 hours, when using a combination of SAMtools and Picard, to about 15 minutes when using elPrep, while utilising the same server resources, here 48 threads and 23GB of RAM. For the same pipeline on whole-genome data (NA12878), elPrep reduces the runtime from 24 hours to less than 5 hours. As a typical clinical study may contain sequencing data for hundreds of patients, elPrep can remove several hundreds of hours of computing time, and thus substantially reduce analysis time and cost

Efficient Data Race Detection for Async-Finish Parallelism

Abstract. A major productivity hurdle for parallel programming is the presence of data races. Data races can lead to all kinds of harmful program behaviors, includ-ing determinism violations and corrupted memory. However, runtime overheads of current dynamic data race detectors are still prohibitively large (often incurring slowdowns of 10 × or larger) for use in mainstream software development. In this paper, we present an efficient dynamic race detector algorithm targeting the async-finish task-parallel parallel programming model. The async and finish constructs are at the core of languages such as X10 and Habanero Java (HJ). These constructs generalize the spawn-sync constructs used in Cilk, while still ensuring that all computation graphs are deadlock-free. We have implemented our algorithm in a tool called TASKCHECKER and eval-uated it on a suite of 12 benchmarks. To reduce overhead of the dynamic analysis, we have also implemented various static optimizations in the tool. Our experi-mental results indicate that our approach performs well in practice, incurring an average slowdown of 3.05 × compared to a serial execution in the optimized case.

Whirlpool: Improving Dynamic Cache Management with Static Data Classification

Cache hierarchies are increasingly non-uniform and difficult to manage. Several techniques, such as scratchpads or reuse hints, use static information about how programs access data to manage the memory hierarchy. Static techniques are effective on regular programs, but because they set fixed policies, they are vulnerable to changes in program behavior or available cache space. Instead, most systems rely on dynamic caching policies that adapt to observed program behavior. Unfortunately, dynamic policies spend significant resources trying to learn how programs use memory, and yet they often perform worse than a static policy. We present Whirlpool, a novel approach that combines static information with dynamic policies to reap the benefits of each. Whirlpool statically classifies data into pools based on how the program uses memory. Whirlpool then uses dynamic policies to tune the cache to each pool. Hence, rather than setting policies statically, Whirlpool uses static analysis to guide dynamic policies. We present both an API that lets programmers specify pools manually and a profiling tool that discovers pools automatically in unmodified binaries. We evaluate Whirlpool on a state-of-the-art NUCA cache. Whirlpool significantly outperforms prior approaches: on sequential programs, Whirlpool improves performance by up to 38% and reduces data movement energy by up to 53%; on parallel programs, Whirlpool improves performance by up to 67% and reduces data movement energy by up to 2.6x.National Science Foundation (U.S.) (grant CCF-1318384)National Science Foundation (U.S.) (CAREER-1452994)Samsung (Firm) (GRO award

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

Virtual Machine Support for Many-Core Architectures: Decoupling Abstract from Concrete Concurrency Models

The upcoming many-core architectures require software developers to exploit concurrency to utilize available computational power. Today's high-level language virtual machines (VMs), which are a cornerstone of software development, do not provide sufficient abstraction for concurrency concepts. We analyze concrete and abstract concurrency models and identify the challenges they impose for VMs. To provide sufficient concurrency support in VMs, we propose to integrate concurrency operations into VM instruction sets. Since there will always be VMs optimized for special purposes, our goal is to develop a methodology to design instruction sets with concurrency support. Therefore, we also propose a list of trade-offs that have to be investigated to advise the design of such instruction sets. As a first experiment, we implemented one instruction set extension for shared memory and one for non-shared memory concurrency. From our experimental results, we derived a list of requirements for a full-grown experimental environment for further research