Performance analysis of a hardware accelerator of dependence management for taskbased dataflow programming models

Along with the popularity of multicore and manycore, task-based dataflow programming models obtain great attention for being able to extract high parallelism from applications without exposing the complexity to programmers. One of these pioneers is the OpenMP Superscalar (OmpSs). By implementing dynamic task dependence analysis, dataflow scheduling and out-of-order execution in runtime, OmpSs achieves high performance using coarse and medium granularity tasks. In theory, for the same application, the more parallel tasks can be exposed, the higher possible speedup can be achieved. Yet this factor is limited by task granularity, up to a point where the runtime overhead outweighs the performance increase and slows down the application. To overcome this handicap, Picos was proposed to support task-based dataflow programming models like OmpSs as a fast hardware accelerator for fine-grained task and dependence management, and a simulator was developed to perform design space exploration. This paper presents the very first functional hardware prototype inspired by Picos. An embedded system based on a Zynq 7000 All-Programmable SoC is developed to study its capabilities and possible bottlenecks. Initial scalability and hardware consumption studies of different Picos designs are performed to find the one with the highest performance and lowest hardware cost. A further thorough performance study is employed on both the prototype with the most balanced configuration and the OmpSs software-only alternative. Results show that our OmpSs runtime hardware support significantly outperforms the software-only implementation currently available in the runtime system for finegrained tasks.This work is supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272) and by the European Research Council RoMoL Grant Agreement number 321253. We also thank the Xilinx University Program for its hardware and software donations.Peer ReviewedPostprint (published version

Analysis of the Task Superscalar architecture hardware design

In this paper, we analyze the operational flow of two hardware implementations of the Task Superscalar architecture. The Task Superscalar is an experimental task based dataflow scheduler that dynamically detects inter-task data dependencies, identifies task-level parallelism, and executes tasks in the out-of-order manner. In this paper, we present a base implementation of the Task Superscalar architecture, as well as a new design with improved performance. We study the behavior of processing some dependent and non-dependent tasks with both base and improved hardware designs and present the simulation results compared with the results of the runtime implementation.This work is supported by the Ministry of Science and Technology of Spain and the European Union (FEDER funds) under contract TIN2007-60625, by the Generalitat de Catalunya (contract 2009-SGR-980), and by the European FP7 project TERAFLUX id. 249013, http://www.tera ux.eu. We would also like to thank the Xilinx University Program for its hardware and software donations.Postprint (author’s final draft

RPPM : Rapid Performance Prediction of Multithreaded workloads on multicore processors

Analytical performance modeling is a useful complement to detailed cycle-level simulation to quickly explore the design space in an early design stage. Mechanistic analytical modeling is particularly interesting as it provides deep insight and does not require expensive offline profiling as empirical modeling. Previous work in mechanistic analytical modeling, unfortunately, is limited to single-threaded applications running on single-core processors. This work proposes RPPM, a mechanistic analytical performance model for multi-threaded applications on multicore hardware. RPPM collects microarchitecture-independent characteristics of a multi-threaded workload to predict performance on a previously unseen multicore architecture. The profile needs to be collected only once to predict a range of processor architectures. We evaluate RPPM's accuracy against simulation and report a performance prediction error of 11.2% on average (23% max). We demonstrate RPPM's usefulness for conducting design space exploration experiments as well as for analyzing parallel application performance

Empirical and Statistical Application Modeling Using on -Chip Performance Monitors.

To analyze the performance of applications and architectures, both programmers and architects desire formal methods to explain anomalous behavior. To this end, we present various methods that utilize non-intrusive, performance-monitoring hardware only recently available on microprocessors to provide further explanations of observed behavior. All the methods attempt to characterize and explain the instruction-level parallelism achieved by codes on different architectures. We also present a prototype tool automating the analysis process to exploit the advantages of the empirical and statistical methods proposed. The empirical, statistical and hybrid methods are discussed and explained with case study results provided. The given methods further the wealth of tools available to programmer\u27s and architects for generally understanding the performance of scientific applications. Specifically, the models and tools presented provide new methods for evaluating and categorizing application performance. The empirical memory model serves to quantify the hierarchical memory performance of applications by inferring the incurred latencies of codes after the effect of latency hiding techniques are realized. The instruction-level model and its extensions model on-chip performance analytically giving insight into inherent performance bottlenecks in superscalar architectures. The statistical model and its hybrid extension provide other methods of categorizing codes via their statistical variations. The PTERA performance tool automates the use of performance counters for use by these methods across platforms making the modeling process easier still. These unique methods provide alternatives to performance modeling and categorizing not available previously in an attempt to utilize the inherent modeling capabilities of performance monitors on commodity processors for scientific applications

RingScalar: A Complexity-Effective Out-of-Order Superscalar Microarchitecture

RingScalar is a complexity-effective microarchitecture for out-of-order superscalar processors, that reduces the area, latency, and power of all major structures in the instruction flow. The design divides an N-way superscalar into N columns connected in a unidirectional ring, where each column contains a portion of the instruction window, a bank of the register file, and an ALU. The design exploits the fact that most decoded instructions are waiting on just one operand to use only a single tag per issue window entry, and to restrict instruction wakeup and value bypass to only communicate with the neighboring column. Detailed simulations of four-issue single-threaded machines running SPECint2000 show that RingScalar has IPC only 13% lower than an idealized superscalar, while providing large reductions in area, power, and circuit latency