Stream Processing for FPGA Accelerators

Total Page:16

File Type:pdf, Size:1020Kb

Stream Processing for FPGA Accelerators International Journal of Research in Engineering, Science and Management 375 Volume-2, Issue-4, April-2019 www.ijresm.com | ISSN (Online): 2581-5792 Stream Processing for FPGA Accelerators Sapana Mahawar1, Ajmeet Singh2 1Student, Department of Electronics and Communication, Poornima College of Engineering, Jaipur, India 2Assistant Professor, Dept. of Electronics and Communication, Poornima College of Engineering, Jaipur, India Abstract: Field-programmable Gate Array (FPGA) running with commitments are made. Diligent years have seen contraptions boast clearing assets with which custom restoring fortified improvement in field-programmable bit gathering head parts for standard, picture, and information administering (FPGA) fortifies, both in the segment of standard contraptions might be seen; in any case, seeing unavoidable, unessential effort vivifying specialists beginning at now requests manual register and their advancement to structure on chip FPGA, setting up exchange level system. heterogeneous multi core processor models. A key inspiration This paper proposes another temperamental directing for the use of FPGA is its capacity to have parts (instigated here structure for FPGA those affirmations to squash this motivation as vivifying authorities) that see unequivocal errands on the driving the control. The tip-top, fine-grained gushing processor, contraption's programmable reason, empowering that known as a spilling fortifying master bundle, is proposed, which progression to be seen with five stars correspondingly as sees resuscitating heads as massive scale custom multi-focus unessential effort. structures. By getting a gushing execution approach with bleeding edge program control and memory paying outstanding personality The central focuses open inside present day FPGA, which to cutoff focuses, major program wasteful points of view can be might be utilized to shape the energizing masters, are stunning: totally disposed of to interface with execution and cost, which are per-second access to trillions of development accumulate stupefying among programming programmable structures. (MAC) activities and bit-level memory zones through on-chip DSP units and square RAM (BRAM). These course of action Keywords: Stream processing, FPGA. FPGA as perfect hosts to unrivaled custom picking models for banner, picture, and information building. Regardless, as the 1. Introduction scale and the refinement of FPGA contraptions increment with Solid years have seen useful improvement in Field each passing age, saddling these focal centers winds up being Programmable Gate Array degrees of headway, both in the sufficient troublesome. This is an out of a general sense portion of standard contraptions and their advancement to the favoring structure, regardless of controls a shocking headway structure on chip FPGA, setting heterogeneous multi center load in perspective on the low piece of plan considering. processor models. A key inspiration for the use of FPGA is its A story gushing restoring master part (SAE) is shown which capacity to have parts (kept up here as vivifying regulators) that embellishments with programming based continuing with see unequivocal errands on the contraption's programmable manager improvement, while keeping up the execution and cost reason, share with that development to be seen with decision of custom circuits. By application to restoring stars for careful correspondingly as immaterial effort. The central focuses open Fourier change (FFT), structure advance (MM), advance inside present day FPGA, which might be utilized to shape the estimation, and sobel edge ID (SED) attracting pros, the reviving stars, are incredible: reliably access to trillions of running with commitments are made. Clear years. progress swarm (MAC) activities and bit-level memory zones have seen vivacious improvement in field-programmable through on-chip DSP units and square RAM (BRAM). These segment pack (FPGA) advancements, both in the segment of cutting FPGA as perfect hosts to unrivaled custom picking standard contraptions and their progress to structure on chip models for standard, picture, and information sorting out. In any FPGA, setting heterogeneous multi core processor models. A case, as the scale and the refinement of FPGA contraptions key inspiration for the utilization of FPGA is its capacity to increment with each passing age, saddling these focal centers have parts (proposed here as reestablishing director) that see winds up being extraordinarily troublesome. This is a unequivocal errands on the contraption's programmable reason, completely influencing structure, at any rate controls a stunning interfacing with that progression to be seen with five stars improvement load by morals of the low bit of plan considering. generally as immaterial effort. A story gushing resuscitating master part (SAE) is indicated The focal centers open inside present day FPGA, which which pulls in programming based vitalizing boss an might be utilized to shape the vitalizing bosses, are magnificent: improvement, while keeping up the execution and cost of per-second access to trillions of development store up (MAC) custom circuits. By application to restoring experts for amazing activities and bit-level memory zones through on-chip DSP Fourier change (FFT), structure improvement (MM), advance units and square RAM (BRAM). These cutting FPGA as perfect estimation, and sobel edge ID (SED) reviving heads, the hosts to unrivaled custom picking models for standard, picture, International Journal of Research in Engineering, Science and Management 376 Volume-2, Issue-4, April-2019 www.ijresm.com | ISSN (Online): 2581-5792 and information arranging. Regardless, as the scale and the programming programmable FPGA answer for this application, refinement of FPGA contraptions increment with each passing and execution and cost were preparing with custom circuit age, saddling these central focuses winds up being sensibly considering. The key bit of the FPE, which hauls in these troublesome. This is an absolutely influencing structure, in any delineations behind spread, is over to the best resource limit. By case controls a dazing improvement load in light of the low bit ensuring full scale most immaterial cost FPE structure, the of plan considering. economies of scale produce super-hot reductions in multi- A story gushing restoring fit part (SAE) is demonstrated center resource cost. Regardless, this brilliant focus goes to the which interfaces with programming based vivifying head enormity of versatility: when joined, the FPE does not show an improvement, while keeping up the execution and cost of on a remarkably key estimation damaged bit of adaptability as custom circuits. By application to restoring stars for dazzling a general questionable processor in light of how the structure is Fourier change (FFT), structure improvement (MM), advance astoundingly obliged at setup time to assist the perfect estimation, and sobel edge ID (SED) vivifying professionals, advancement with most major execution and least cost; the running with responsibilities are made. consequently, while it may be reconsidered after association, it can't interface all around key undertaking in the framework for 2. FPGA processing element a standard delicate focus interest. Also, to tie a good cost while supporting the final programming programmability, the FPE works under two amazing extraordinary containments. 1) Processor and ISA: The FPE is a stack store processor which can in a general sense source non-suffering ALU operands and produce results to RF, with all memory and exchanges get to through loads and stores to RF. 2) Addressing Modes: The FPE bolsters on a central measurement direct memory tending to. Fig. 1. FPE (a) FPE SISD mode, (b) FPE SIMD mode The FPE is a decreased bearing set PC load store PE, SIMD, and single course, single data (SISD) (i.e., single-way SIMD) Fig. 2. FPE based SD for 4 × 4 802.11n parties. It houses a program counter, program memory (PM), bearing decoder, register report (RF), branch a zone, DM, brief 3. Stream processing for FPGA accelerators memory, and an ALU subject to the DSP48E in Xilinx FPGA. A COMM module allows direct circuit/extraction of data into The pile store FPE bolsters just register– register and brief and out of the FPE pipeline. The FPE is lean, joining only those guidelines; this proposes all non-clear operands and results get zones key to programming programmability what's more is to the ALU by systems for RF. Consider the effect of this need wonderfully configurable for unimportant exertion request the concerning a 256-point FFT seen by structures for two FPE asked about is proposed for detail on bearing set objectivity searches for after: an eight-way FPE SIMD or a MIMD multi- (ISA) and configurability. The result is unendingly certain FPE made out of eight SISD FPEs. The FFT mappings and the reasonably a 16-bit SISD FPE on Xilinx Virtex 5 props 480 oversaw ALU, between processor correspondence, memory MMACs/s requiring 90 question tables just 14% of the cost of (MEM), and NOP rules for each are showed up in Fig. 3. As a Micro burst and 35% of that of the iDEA lean processor on a showed up in Fig. 3, the upside of these activities is low in a flawed contraption, while the 10×10 unequivocally coupled general sense 52.5% and 31.8% of the specific cycles. processor gathering (TCPA) multi center eats up 134 570 These results are a short surrendered consequence of LUTs. This execution/cost blend improved a multi-FPE permitting just register– register headings gathering. Each reestablishing boss for help clearing in 4×4 limiting information FFT256 plan, and along these lines each FPE cycle, eats with the two befuddling features. it is the central pushing up/produces 512 complex words. Since RF is the most resource International Journal of Research in Engineering, Science and Management 377 Volume-2, Issue-4, April-2019 www.ijresm.com | ISSN (Online): 2581-5792 over the best bit of the FPE, buffering this volume of data closer and dynamic data among DM and COMM, diminishing requires BRAM DM; all together for these operands to be structure abundance unfathomably more. managed and results set away and unbelievable are required among BRAM and RF.
Recommended publications
  • Parallel Patterns for Adaptive Data Stream Processing
    Università degli Studi di Pisa Dipartimento di Informatica Dottorato di Ricerca in Informatica Ph.D. Thesis Parallel Patterns for Adaptive Data Stream Processing Tiziano De Matteis Supervisor Supervisor Marco Danelutto Marco Vanneschi Abstract In recent years our ability to produce information has been growing steadily, driven by an ever increasing computing power, communication rates, hardware and software sensors diffusion. is data is often available in the form of continuous streams and the ability to gather and analyze it to extract insights and detect patterns is a valu- able opportunity for many businesses and scientific applications. e topic of Data Stream Processing (DaSP) is a recent and highly active research area dealing with the processing of this streaming data. e development of DaSP applications poses several challenges, from efficient algorithms for the computation to programming and runtime systems to support their execution. In this thesis two main problems will be tackled: • need for high performance: high throughput and low latency are critical re- quirements for DaSP problems. Applications necessitate taking advantage of parallel hardware and distributed systems, such as multi/manycores or cluster of multicores, in an effective way; • dynamicity: due to their long running nature (24hr/7d), DaSP applications are affected by highly variable arrival rates and changes in their workload charac- teristics. Adaptivity is a fundamental feature in this context: applications must be able to autonomously scale the used resources to accommodate dynamic requirements and workload while maintaining the desired Quality of Service (QoS) in a cost-effective manner. In the current approaches to the development of DaSP applications are still miss- ing efficient exploitation of intra-operator parallelism as well as adaptations strategies with well known properties of stability, QoS assurance and cost awareness.
    [Show full text]
  • A Middleware for Efficient Stream Processing in CUDA
    Noname manuscript No. (will be inserted by the editor) A Middleware for E±cient Stream Processing in CUDA Shinta Nakagawa ¢ Fumihiko Ino ¢ Kenichi Hagihara Received: date / Accepted: date Abstract This paper presents a middleware capable of which are expressed as kernels. On the other hand, in- out-of-order execution of kernels and data transfers for put/output data is organized as streams, namely se- e±cient stream processing in the compute uni¯ed de- quences of similar data records. Input streams are then vice architecture (CUDA). Our middleware runs on the passed through the chain of kernels in a pipelined fash- CUDA-compatible graphics processing unit (GPU). Us- ion, producing output streams. One advantage of stream ing the middleware, application developers are allowed processing is that it can exploit the parallelism inherent to easily overlap kernel computation with data trans- in the pipeline. For example, the execution of the stages fer between the main memory and the video memory. can be overlapped with each other to exploit task par- To maximize the e±ciency of this overlap, our middle- allelism. Furthermore, di®erent stream elements can be ware performs out-of-order execution of commands such simultaneously processed to exploit data parallelism. as kernel invocations and data transfers. This run-time One of the stream architecture that bene¯t from capability can be used by just replacing the original the advantages mentioned above is the graphics pro- CUDA API calls with our API calls. We have applied cessing unit (GPU), originally designed for acceleration the middleware to a practical application to understand of graphics applications.
    [Show full text]
  • AMD Accelerated Parallel Processing Opencl Programming Guide
    AMD Accelerated Parallel Processing OpenCL Programming Guide November 2013 rev2.7 © 2013 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo, AMD Accelerated Parallel Processing, the AMD Accelerated Parallel Processing logo, ATI, the ATI logo, Radeon, FireStream, FirePro, Catalyst, and combinations thereof are trade- marks of Advanced Micro Devices, Inc. Microsoft, Visual Studio, Windows, and Windows Vista are registered trademarks of Microsoft Corporation in the U.S. and/or other jurisdic- tions. Other names are for informational purposes only and may be trademarks of their respective owners. OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos. The contents of this document are provided in connection with Advanced Micro Devices, Inc. (“AMD”) products. AMD makes no representations or warranties with respect to the accuracy or completeness of the contents of this publication and reserves the right to make changes to specifications and product descriptions at any time without notice. The information contained herein may be of a preliminary or advance nature and is subject to change without notice. No license, whether express, implied, arising by estoppel or other- wise, to any intellectual property rights is granted by this publication. Except as set forth in AMD’s Standard Terms and Conditions of Sale, AMD assumes no liability whatsoever, and disclaims any express or implied warranty, relating to its products including, but not limited to, the implied warranty of merchantability, fitness for a particular purpose, or infringement of any intellectual property right. AMD’s products are not designed, intended, authorized or warranted for use as compo- nents in systems intended for surgical implant into the body, or in other applications intended to support or sustain life, or in any other application in which the failure of AMD’s product could create a situation where personal injury, death, or severe property or envi- ronmental damage may occur.
    [Show full text]
  • Fine-Grained Window-Based Stream Processing on CPU-GPU Integrated
    FineStream: Fine-Grained Window-Based Stream Processing on CPU-GPU Integrated Architectures Feng Zhang and Lin Yang, Renmin University of China; Shuhao Zhang, Technische Universität Berlin and National University of Singapore; Bingsheng He, National University of Singapore; Wei Lu and Xiaoyong Du, Renmin University of China https://www.usenix.org/conference/atc20/presentation/zhang-feng This paper is included in the Proceedings of the 2020 USENIX Annual Technical Conference. July 15–17, 2020 978-1-939133-14-4 Open access to the Proceedings of the 2020 USENIX Annual Technical Conference is sponsored by USENIX. FineStream: Fine-Grained Window-Based Stream Processing on CPU-GPU Integrated Architectures Feng Zhang1, Lin Yang1, Shuhao Zhang2;3, Bingsheng He3, Wei Lu1, Xiaoyong Du1 1Key Laboratory of Data Engineering and Knowledge Engineering (MOE), and School of Information, Renmin University of China 2DIMA, Technische Universität Berlin 3School of Computing, National University of Singapore [email protected], [email protected], [email protected], [email protected], [email protected], [email protected] Abstract GPU memory via PCI-e before GPU processing, but the low Accelerating SQL queries on stream processing by utilizing bandwidth of PCI-e limits the performance of stream process- heterogeneous coprocessors, such as GPUs, has shown to be ing on GPUs. Hence, stream processing on GPUs needs to be an effective approach. Most works show that heterogeneous carefully designed to hide the PCI-e overhead. For example, coprocessors bring significant performance improvement be- prior works have explored pipelining the computation and cause of their high parallelism and computation capacity.
    [Show full text]
  • Lightsaber: Efficient Window Aggregation on Multi-Core Processors
    Research 28: Stream Processing SIGMOD ’20, June 14–19, 2020, Portland, OR, USA LightSaber: Efficient Window Aggregation on Multi-core Processors Georgios Theodorakis Alexandros Koliousis∗ Peter Pietzuch Imperial College London Graphcore Research Holger Pirk [email protected] [email protected] [email protected] [email protected] Imperial College London Abstract Invertible Aggregation (Sum) Non-Invertible Aggregation (Min) 200 Window aggregation queries are a core part of streaming ap- 140 150 Multiple TwoStacks 120 tuples/s) Multiple TwoStacks plications. To support window aggregation efficiently, stream 6 Multiple SoE 100 SlickDeque SlickDeque 80 100 SlideSide SlideSide processing engines face a trade-off between exploiting par- 60 allelism (at the instruction/multi-core levels) and incremen- 50 40 20 tal computation (across overlapping windows and queries). 0 0 Throughput (10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Existing engines implement ad-hoc aggregation and par- # Queries # Queries allelization strategies. As a result, they only achieve high Figure 1: Evaluating window aggregation queries performance for specific queries depending on the window definition and the type of aggregation function. an order of magnitude higher throughput compared to ex- We describe a general model for the design space of win- isting systems—on a 16-core server, it processes 470 million dow aggregation strategies. Based on this, we introduce records/s with 132 µs average latency. LightSaber, a new stream processing engine that balances parallelism and incremental processing when executing win- CCS Concepts dow aggregation queries on multi-core CPUs.
    [Show full text]
  • Parallel Stream Processing with MPI for Video Analytics and Data Visualization
    Parallel Stream Processing with MPI for Video Analytics and Data Visualization Adriano Vogel1 [0000−0003−3299−2641], Cassiano Rista1, Gabriel Justo1, Endrius Ewald1, Dalvan Griebler1;3[0000−0002−4690−3964], Gabriele Mencagli2, and Luiz Gustavo Fernandes1[0000−0002−7506−3685] 1 School of Technology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre - Brazil [email protected] 2 Department of Computer Science, University of Pisa, Pisa - Italy 3 Laboratory of Advanced Research on Cloud Computing (LARCC), Tr^esde Maio Faculty (SETREM), Tr^esde Maio - Brazil Abstract. The amount of data generated is increasing exponentially. However, processing data and producing fast results is a technological challenge. Parallel stream processing can be implemented for handling high frequency and big data flows. The MPI parallel programming model offers low-level and flexible mechanisms for dealing with distributed ar- chitectures such as clusters. This paper aims to use it to accelerate video analytics and data visualization applications so that insight can be ob- tained as soon as the data arrives. Experiments were conducted with a Domain-Specific Language for Geospatial Data Visualization and a Person Recognizer video application. We applied the same stream par- allelism strategy and two task distribution strategies. The dynamic task distribution achieved better performance than the static distribution in the HPC cluster. The data visualization achieved lower throughput with respect to the video analytics due to the I/O intensive operations. Also, the MPI programming model shows promising performance outcomes for stream processing applications. Keywords: parallel programming, stream parallelism, distributed pro- cessing, cluster 1 Introduction Nowadays, we are assisting to an explosion of devices producing data in the form of unbounded data streams that must be collected, stored, and processed in real-time [12].
    [Show full text]
  • Design and Implementation of an FPGA-Based Scalable Pipelined
    Design and Implementation of an FPGA-Based Scalable Pipelined Associative SIMD Processor Array with Specialized Variations for Sequence Comparison and MSIMD Operation A dissertation submitted to the Kent State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Hong Wang November 2006 Dissertation written by Hong Wang B.A., Lanzhou University, 1990 M.A., Kent State University, 2002 Ph.D., Kent State University, 2006 Approved by ___Robert A. Walker ___________________, Chair, Doctoral Dissertation Committee ___Johnnie W. Baker ___________________, Members, Doctoral Dissertation Committee ___Kenneth E Batcher __________________ ___Eugene C. Gartland, Jr._______________ _____________________________________ Accepted by ___Robert A. Walker___________________, Chair, Department of Computer Science ___John R. D. Stalvey__________________, Dean, College of Arts and Sciences ii Table of Contents 1 INTRODUCTION........................................................................................................... 3 1.1 Overview of Associative Computing Research......................................................... 5 1.2 Associative Computing ............................................................................................... 7 1.2.1 Associative Search and Responder Processing......................................................... 7 1.2.2 Associative Reduction for Maximum / Minimum Value ......................................... 9 1.3 Applications of a Scalable Associative Processor..................................................
    [Show full text]
  • SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures
    SABER: Window-Based Hybrid Stream Processing for Heterogeneous Architectures Alexandros Koliousis†, Matthias Weidlich†‡, Raul Castro Fernandez†, Alexander L. Wolf†, Paolo Costa], Peter Pietzuch† †Imperial College London ‡Humboldt-Universität zu Berlin ]Microsoft Research {akolious, mweidlic, rc3011, alw, costa, prp}@imperial.ac.uk ABSTRACT offered by heterogeneous servers: besides multi-core CPUs with Modern servers have become heterogeneous, often combining multi- dozens of processing cores, such servers also contain accelerators core CPUs with many-core GPGPUs. Such heterogeneous architec- such as GPGPUs with thousands of cores. As heterogeneous servers tures have the potential to improve the performance of data-intensive have become commonplace in modern data centres and cloud offer- stream processing applications, but they are not supported by cur- ings [24], researchers have proposed a range of parallel streaming al- rent relational stream processing engines. For an engine to exploit a gorithms for heterogeneous architectures, including for joins [36,51], heterogeneous architecture, it must execute streaming SQL queries sorting [28] and sequence alignment [42]. Yet, we lack the design with sufficient data-parallelism to fully utilise all available heteroge- of a general-purpose relational stream processing engine that can neous processors, and decide how to use each in the most effective transparently take advantage of accelerators such as GPGPUs while way. It must do this while respecting the semantics of streaming executing arbitrary streaming SQL queries with windowing [7]. SQL queries, in particular with regard to window handling. The design of such a streaming engine for heterogeneous archi- tectures raises three challenges, addressed in this paper: We describe SABER, a hybrid high-performance relational stream processing engine for CPUs and GPGPUs.
    [Show full text]
  • Analyzing Efficient Stream Processing on Modern Hardware
    Analyzing Efficient Stream Processing on Modern Hardware Steffen Zeuch 2, Bonaventura Del Monte 2, Jeyhun Karimov 2, Clemens Lutz 2, Manuel Renz 2, Jonas Traub 1, Sebastian Breß 1;2, Tilmann Rabl 1;2, Volker Markl 1;2 1Technische Universitat¨ Berlin, 2German Research Center for Artificial Intelligence fi[email protected] 384 Flink ABSTRACT 420 Memory Bandwidth 288 Spark Modern Stream Processing Engines (SPEs) process large 100 71 23:99 28 Storm data volumes under tight latency constraints. Many SPEs Java HC 10 6:25 execute processing pipelines using message passing on shared- 3:6 C++ HC Streambox Throughput nothing architectures and apply a partition-based scale-out [M records/s] 1 0:26 Saber strategy to handle high-velocity input streams. Further- Optimized more, many state-of-the-art SPEs rely on a Java Virtual Ma- Figure 1: Yahoo! Streaming Benchmark (1 Node). chine to achieve platform independence and speed up system development by abstracting from the underlying hardware. a large number of computing nodes in a cluster, i.e., scale- In this paper, we show that taking the underlying hard- out the processing. These systems trade single node perfor- ware into account is essential to exploit modern hardware mance for scalability to large clusters and use a Java Virtual efficiently. To this end, we conduct an extensive experimen- Machine (JVM) as the underlying processing environment tal analysis of current SPEs and SPE design alternatives for platform independence. While JVMs provide a high level optimized for modern hardware. Our analysis highlights po- of abstraction from the underlying hardware, they cannot tential bottlenecks and reveals that state-of-the-art SPEs are easily provide efficient data access due to processing over- not capable of fully exploiting current and emerging hard- heads induced by data (de-)serialization, objects scattering ware trends, such as multi-core processors and high-speed in main memory, virtual functions, and garbage collection.
    [Show full text]
  • Streambox: Modern Stream Processing on a Multicore Machine
    StreamBox: Modern Stream Processing on a Multicore Machine Hongyu Miao and Heejin Park, Purdue ECE; Myeongjae Jeon and Gennady Pekhimenko, Microsoft Research; Kathryn S. McKinley, Google; Felix Xiaozhu Lin, Purdue ECE https://www.usenix.org/conference/atc17/technical-sessions/presentation/miao This paper is included in the Proceedings of the 2017 USENIX Annual Technical Conference (USENIX ATC ’17). July 12–14, 2017 • Santa Clara, CA, USA ISBN 978-1-931971-38-6 Open access to the Proceedings of the 2017 USENIX Annual Technical Conference is sponsored by USENIX. StreamBox: Modern Stream Processing on a Multicore Machine Hongyu Miao1, Heejin Park1, Myeongjae Jeon2, Gennady Pekhimenko2, Kathryn S. McKinley3, and Felix Xiaozhu Lin1 1Purdue ECE 2Microsoft Research 3Google Abstract Most stream processing engines are distributed be- cause they assume processing requirements outstrip the Stream analytics on real-time events has an insatiable de- capabilities of a single machine [38, 28, 32]. How- mand for throughput and latency. Its performance on a ever, modern hardware advances make a single multi- single machine is central to meeting this demand, even in core machine an attractive streaming platform. These ad- a distributed system. This paper presents a novel stream vances include (i) high throughput I/O that significantly StreamBox processing engine called that exploits the improves ingress rate, e.g., Remote Direct Memory Ac- parallelism and memory hierarchy of modern multicore cess (RDMA) and 10Gb Ethernet; (ii) terabyte DRAMs StreamBox hardware. executes a pipeline of transforms that hold massive in-memory stream processing state; over records that may arrive out-of-order. As records ar- and (iii) a large number of cores.
    [Show full text]
  • A Media Enhanced Vector Architecture for Embedded Memory Systems
    A Media-Enhanced Vector Architecture for Embedded Memory Systems Christoforos Kozyrakis Report No. UCB/CSD-99-1059 July 1999 Computer Science Division (EECS) University of California Berkeley, California 94720 A Media-Enhanced Vector Architecture for Embedded Memory Systems by Christoforos Kozyrakis Research Project Submitted to the Department of Electrical Engineering and Computer Sciences, Uni- versity of California at Berkeley, in partial satisfaction of the requirements for the degree of Master of Science, Plan II. Approval for the Report and Comprehensive Examination: Committee: Professor David A. Patterson Research Advisor (Date) ******* Professor Katherine Yelick Second Reader (Date) A Media-Enhanced Vector Architecture for Embedded Memory Systems Christoforos Kozyrakis M.S. Report Abstract Next generation portable devices will require processors with both low energy consump- tion and high performance for media functions. At the same time, modern CMOS technology creates the need for highly scalable VLSI architectures. Conventional processor architec- tures fail to meet these requirements. This paper presents the architecture of Vector IRAM (VIRAM), a processor that combines vector processing with embedded DRAM technology. Vector processing achieves high multimedia performance with simple hardware, while em- bedded DRAM provides high memory bandwidth at low energy consumption. VIRAM pro- vides ¯exible support for media data types, short vectors, and DSP features. The vector pipeline is enhanced to hide DRAM latency without using caches. The peak performance is 3.2 GFLOPS (single precision) and maximum memory bandwidth is 25.6 GBytes/s. With a target power consumption of 2 Watts for the vector pipeline and the memory system, VIRAM supports 1.6 GFLOPS/Watt. For a set of representative media kernels, VIRAM sustains on average 88% of its peak performance, outperforming conventional SIMD media extensions and DSP processors by factors of 4.5 to 17.
    [Show full text]
  • Fine-Grained Real-Time Stream Processing with Cameo
    Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo Le Xu, University of Illinois at Urbana-Champaign; Shivaram Venkataraman, UW-Madison; Indranil Gupta, University of Illinois at Urbana-Champaign; Luo Mai, University of Edinburgh; Rahul Potharaju, Microsoft https://www.usenix.org/conference/nsdi21/presentation/xu This paper is included in the Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation. April 12–14, 2021 978-1-939133-21-2 Open access to the Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation is sponsored by Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo Le Xu1∗, Shivaram Venkataraman2, Indranil Gupta1, Luo Mai3, and Rahul Potharaju4 1University of Illinois at Urbana-Champaign, 2UW-Madison, 3University of Edinburgh, 4Microsoft Abstract Resource provisioning in multi-tenant stream processing sys- tems faces the dual challenges of keeping resource utilization high (without over-provisioning), and ensuring performance isolation. In our common production use cases, where stream- ing workloads have to meet latency targets and avoid breach- ing service-level agreements, existing solutions are incapable Figure 1: Slot-based system (Flink), Simple Actor system (Or- of handling the wide variability of user needs. Our framework leans), and our framework Cameo. called Cameo uses fine-grained stream processing (inspired by actor computation models), and is able to provide high latency. Others wish to maximize throughput under limited re- resource utilization while meeting latency targets. Cameo dy- sources, and yet others desire high resource utilization. Violat- namically calculates and propagates priorities of events based ing such user expectations is expensive, resulting in breaches on user latency targets and query semantics.
    [Show full text]