Stream Processing on High-Bandwidth Memory
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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. -
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. -
Performance Impact of Memory Channels on Sparse and Irregular Algorithms
Performance Impact of Memory Channels on Sparse and Irregular Algorithms Oded Green1,2, James Fox2, Jeffrey Young2, Jun Shirako2, and David Bader3 1NVIDIA Corporation — 2Georgia Institute of Technology — 3New Jersey Institute of Technology Abstract— Graph processing is typically considered to be a Graph algorithms are typically latency-bound if there is memory-bound rather than compute-bound problem. One com- not enough parallelism to saturate the memory subsystem. mon line of thought is that more available memory bandwidth However, the growth in parallelism for shared-memory corresponds to better graph processing performance. However, in this work we demonstrate that the key factor in the utilization systems brings into question whether graph algorithms are of the memory system for graph algorithms is not necessarily still primarily latency-bound. Getting peak or near-peak the raw bandwidth or even the latency of memory requests. bandwidth of current memory subsystems requires highly Instead, we show that performance is proportional to the parallel applications as well as good spatial and temporal number of memory channels available to handle small data memory reuse. The lack of spatial locality in sparse and transfers with limited spatial locality. Using several widely used graph frameworks, including irregular algorithms means that prefetched cache lines have Gunrock (on the GPU) and GAPBS & Ligra (for CPUs), poor data reuse and that the effective bandwidth is fairly low. we evaluate key graph analytics kernels using two unique The introduction of high-bandwidth memories like HBM and memory hierarchies, DDR-based and HBM/MCDRAM. Our HMC have not yet closed this inefficiency gap for latency- results show that the differences in the peak bandwidths sensitive accesses [19]. -
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. -
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. -
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. -
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]. -
Case Study on Integrated Architecture for In-Memory and In-Storage Computing
electronics Article Case Study on Integrated Architecture for In-Memory and In-Storage Computing Manho Kim 1, Sung-Ho Kim 1, Hyuk-Jae Lee 1 and Chae-Eun Rhee 2,* 1 Department of Electrical Engineering, Seoul National University, Seoul 08826, Korea; [email protected] (M.K.); [email protected] (S.-H.K.); [email protected] (H.-J.L.) 2 Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea * Correspondence: [email protected]; Tel.: +82-32-860-7429 Abstract: Since the advent of computers, computing performance has been steadily increasing. Moreover, recent technologies are mostly based on massive data, and the development of artificial intelligence is accelerating it. Accordingly, various studies are being conducted to increase the performance and computing and data access, together reducing energy consumption. In-memory computing (IMC) and in-storage computing (ISC) are currently the most actively studied architectures to deal with the challenges of recent technologies. Since IMC performs operations in memory, there is a chance to overcome the memory bandwidth limit. ISC can reduce energy by using a low power processor inside storage without an expensive IO interface. To integrate the host CPU, IMC and ISC harmoniously, appropriate workload allocation that reflects the characteristics of the target application is required. In this paper, the energy and processing speed are evaluated according to the workload allocation and system conditions. The proof-of-concept prototyping system is implemented for the integrated architecture. The simulation results show that IMC improves the performance by 4.4 times and reduces total energy by 4.6 times over the baseline host CPU. -
ECE 571 – Advanced Microprocessor-Based Design Lecture 17
ECE 571 { Advanced Microprocessor-Based Design Lecture 17 Vince Weaver http://web.eece.maine.edu/~vweaver [email protected] 3 April 2018 Announcements • HW8 is readings 1 More DRAM 2 ECC Memory • There's debate about how many errors can happen, anywhere from 10−10 error/bit*h (roughly one bit error per hour per gigabyte of memory) to 10−17 error/bit*h (roughly one bit error per millennium per gigabyte of memory • Google did a study and they found more toward the high end • Would you notice if you had a bit flipped? • Scrubbing { only notice a flip once you read out a value 3 Registered Memory • Registered vs Unregistered • Registered has a buffer on board. More expensive but can have more DIMMs on a channel • Registered may be slower (if it buffers for a cycle) • RDIMM/UDIMM 4 Bandwidth/Latency Issues • Truly random access? No, burst speed fast, random speed not. • Is that a problem? Mostly filling cache lines? 5 Memory Controller • Can we have full random access to memory? Why not just pass on CPU mem requests unchanged? • What might have higher priority? • Why might re-ordering the accesses help performance (back and forth between two pages) 6 Reducing Refresh • DRAM Refresh Mechanisms, Penalties, and Trade-Offs by Bhati et al. • Refresh hurts performance: ◦ Memory controller stalls access to memory being refreshed ◦ Refresh takes energy (read/write) On 32Gb device, up to 20% of energy consumption and 30% of performance 7 Async vs Sync Refresh • Traditional refresh rates ◦ Async Standard (15.6us) ◦ Async Extended (125us) ◦ SDRAM - -
Adm-Pcie-9H3 V1.5
ADM-PCIE-9H3 10th September 2019 Datasheet Revision: 1.5 AD01365 Applications Board Features • High-Performance Network Accelerator • 1x OpenCAPI Interface • Data CenterData Processor • 1x QSFP-DD Cages • High Performance Computing (HPC) • Shrouded heatsink with passive and fan cooling • System Modelling options • Market Analysis FPGA Features • 2x 4GB HBM Gen2 memory (32 AXI Ports provide 460GB/s Access Bandwidth) • 3x 100G Ethernet MACs (incl. KR4 RS-FEC) • 3x 150G Interlaken cores • 2x PCI Express x16 Gen3 / x8 Gen4 cores Summary The ADM-PCIE-9H3 is a high-performance FPGA processing card intended for data center applications using Virtex UltraScale+ High Bandwidth Memory FPGAs from Xilinx. The ADM-PCIE-9H3 utilises the Xilinx Virtex Ultrascale Plus FPGA family that includes on substrate High Bandwidth Memory (HBM Gen2). This provides exceptional memory Read/Write performance while reducing the overall power consumption of the board by negating the need for external SDRAM devices. There are also a number of high speed interface options available including 100G Ethernet MACs and OpenCAPI connectivity, to make the most of these interfaces the ADM-PCIE-9H3 is fitted with a QSFP-DD Cage (8x28Gbps lanes) and one OpenCAPI interface for ultra low latency communications. Target Device Host Interface Xilinx Virtex UltraScale Plus: XCVU33P-2E 1x PCI Express Gen3 x16 or 1x/2x* PCI Express (FSVH2104) Gen4 x8 or OpenCAPI LUTs = 440k(872k) Board Format FFs = 879k(1743k) DSPs = 2880(5952) 1/2 Length low profile x16 PCIe form Factor BRAM = 23.6Mb(47.3Mb) WxHxD = 19.7mm x 80.1mm x 181.5mm URAM = 90.0Mb(180.0Mb) Weight = TBCg 2x 4GB HBM Gen2 memory (32 AXI Ports Communications Interfaces provide 460GB/s Access Bandwidth) 1x QSFP-DD 8x28Gbps - 10/25/40/100G 3x 100G Ethernet MACs (incl. -
High Bandwidth Memory for Graphics Applications Contents
High Bandwidth Memory for Graphics Applications Contents • Differences in Requirements: System Memory vs. Graphics Memory • Timeline of Graphics Memory Standards • GDDR2 • GDDR3 • GDDR4 • GDDR5 SGRAM • Problems with GDDR • Solution ‐ Introduction to HBM • Performance comparisons with GDDR5 • Benchmarks • Hybrid Memory Cube Differences in Requirements System Memory Graphics Memory • Optimized for low latency • Optimized for high bandwidth • Short burst vector loads • Long burst vector loads • Equal read/write latency ratio • Low read/write latency ratio • Very general solutions and designs • Designs can be very haphazard Brief History of Graphics Memory Types • Ancient History: VRAM, WRAM, MDRAM, SGRAM • Bridge to modern times: GDDR2 • The first modern standard: GDDR4 • Rapidly outclassed: GDDR4 • Current state: GDDR5 GDDR2 • First implemented with Nvidia GeForce FX 5800 (2003) • Midway point between DDR and ‘true’ DDR2 • Stepping stone towards DDR‐based graphics memory • Second‐generation GDDR2 based on DDR2 GDDR3 • Designed by ATI Technologies , first used by Nvidia GeForce FX 5700 (2004) • Based off of the same technological base as DDR2 • Lower heat and power consumption • Uses internal terminators and a 32‐bit bus GDDR4 • Based on DDR3, designed by Samsung from 2005‐2007 • Introduced Data Bus Inversion (DBI) • Doubled prefetch size to 8n • Used on ATI Radeon 2xxx and 3xxx, never became commercially viable GDDR5 SGRAM • Based on DDR3 SDRAM memory • Inherits benefits of GDDR4 • First used in AMD Radeon HD 4870 video cards (2008) • Current -
Architectures for High Performance Computing and Data Systems Using Byte-Addressable Persistent Memory
Architectures for High Performance Computing and Data Systems using Byte-Addressable Persistent Memory Adrian Jackson∗, Mark Parsonsy, Michele` Weilandz EPCC, The University of Edinburgh Edinburgh, United Kingdom ∗[email protected], [email protected], [email protected] Bernhard Homolle¨ x SVA System Vertrieb Alexander GmbH Paderborn, Germany x [email protected] Abstract—Non-volatile, byte addressable, memory technology example of such hardware, used for data storage. More re- with performance close to main memory promises to revolutionise cently, flash memory has been used for high performance I/O computing systems in the near future. Such memory technology in the form of Solid State Disk (SSD) drives, providing higher provides the potential for extremely large memory regions (i.e. > 3TB per server), very high performance I/O, and new ways bandwidth and lower latency than traditional Hard Disk Drives of storing and sharing data for applications and workflows. (HDD). This paper outlines an architecture that has been designed to Whilst flash memory can provide fast input/output (I/O) exploit such memory for High Performance Computing and High performance for computer systems, there are some draw backs. Performance Data Analytics systems, along with descriptions of It has limited endurance when compare to HDD technology, how applications could benefit from such hardware. Index Terms—Non-volatile memory, persistent memory, stor- restricted by the number of modifications a memory cell age class memory, system architecture, systemware, NVRAM, can undertake and thus the effective lifetime of the flash SCM, B-APM storage [29]. It is often also more expensive than other storage technologies.