Slide at OpenStack Summit 2018 Vancouver
Session Info and Video: https://www.openstack.org/videos/vancouver-2018/can-we-boost-more-hpc-performance-integrate-ibm-power-servers-with-gpus-to-openstack-environment
Slides at OpenStack Summit 2017 Sydney
Session Info and Video: https://www.openstack.org/videos/sydney-2017/100gbps-openstack-for-providing-high-performance-nfv
Jetson AGX Xavier and the New Era of Autonomous MachinesDustin Franklin
Deep-dive on NVIDIA Jetson AGX Xavier, designed to help you deploy advanced AI onboard robots, drones, and other autonomous machines. View the webinar here: https://bit.ly/2BWVWv1
Part 3 Maximizing the utilization of GPU resources on-premise and in the cloudUniva, an Altair Company
This document discusses Multi-Instance GPU (MIG) on the NVIDIA A100 GPU. MIG allows a single GPU to be partitioned into multiple isolated GPU instances. This increases GPU utilization by right-sizing allocations to workloads. MIG provides benefits like guaranteed quality of service, versatile profiles to maximize utilization, and no required code changes. MIG is well-suited for workloads that don't use the full GPU, like HPC, prototyping, inference, and light training. It allows optimizing GPU utilization and expanding access to more users simultaneously with predictable performance.
The document discusses NVIDIA's new Volta GPU architecture and its Tesla V100 GPU. Some key points:
- The Tesla V100 GPU uses the new Volta architecture and features new Tensor Cores that provide a major speedup for deep learning workloads.
- Compared to the previous Pascal GPU, the V100 offers 6x higher deep learning performance using FP16 and 1.5-1.9x higher performance for FP32 and FP64 workloads.
- The V100's Tensor Cores enable mixed precision training where most operations can be done in FP16 with no loss of accuracy using techniques like loss scaling.
- Benchmark results show training ResNet-50 on
This document summarizes a presentation on introducing the Persistent Memory Development Kit (PMDK) into PostgreSQL to utilize persistent memory (PMEM). The presentation covers: (1) hacking the PostgreSQL write-ahead log (WAL) and relation files to directly memory copy to PMEM, (2) evaluating the hacks which showed a 3% improvement to transactions and 30% reduction to checkpoint time, and (3) tips for PMEM programming like cache flushing and avoiding volatile layers.
Hardware for deep learning includes CPUs, GPUs, FPGAs, and ASICs. CPUs are general purpose but support deep learning through instructions like AVX-512 and libraries. GPUs like NVIDIA and AMD models are commonly used due to high parallelism and memory bandwidth. FPGAs offer high efficiency but require specialized programming. ASICs like Google's TPU are customized for deep learning and provide high performance but limited flexibility. Emerging hardware aims to improve efficiency and better match neural network computations.
Slides at OpenStack Summit 2017 Sydney
Session Info and Video: https://www.openstack.org/videos/sydney-2017/100gbps-openstack-for-providing-high-performance-nfv
Jetson AGX Xavier and the New Era of Autonomous MachinesDustin Franklin
Deep-dive on NVIDIA Jetson AGX Xavier, designed to help you deploy advanced AI onboard robots, drones, and other autonomous machines. View the webinar here: https://bit.ly/2BWVWv1
Part 3 Maximizing the utilization of GPU resources on-premise and in the cloudUniva, an Altair Company
This document discusses Multi-Instance GPU (MIG) on the NVIDIA A100 GPU. MIG allows a single GPU to be partitioned into multiple isolated GPU instances. This increases GPU utilization by right-sizing allocations to workloads. MIG provides benefits like guaranteed quality of service, versatile profiles to maximize utilization, and no required code changes. MIG is well-suited for workloads that don't use the full GPU, like HPC, prototyping, inference, and light training. It allows optimizing GPU utilization and expanding access to more users simultaneously with predictable performance.
The document discusses NVIDIA's new Volta GPU architecture and its Tesla V100 GPU. Some key points:
- The Tesla V100 GPU uses the new Volta architecture and features new Tensor Cores that provide a major speedup for deep learning workloads.
- Compared to the previous Pascal GPU, the V100 offers 6x higher deep learning performance using FP16 and 1.5-1.9x higher performance for FP32 and FP64 workloads.
- The V100's Tensor Cores enable mixed precision training where most operations can be done in FP16 with no loss of accuracy using techniques like loss scaling.
- Benchmark results show training ResNet-50 on
This document summarizes a presentation on introducing the Persistent Memory Development Kit (PMDK) into PostgreSQL to utilize persistent memory (PMEM). The presentation covers: (1) hacking the PostgreSQL write-ahead log (WAL) and relation files to directly memory copy to PMEM, (2) evaluating the hacks which showed a 3% improvement to transactions and 30% reduction to checkpoint time, and (3) tips for PMEM programming like cache flushing and avoiding volatile layers.
Hardware for deep learning includes CPUs, GPUs, FPGAs, and ASICs. CPUs are general purpose but support deep learning through instructions like AVX-512 and libraries. GPUs like NVIDIA and AMD models are commonly used due to high parallelism and memory bandwidth. FPGAs offer high efficiency but require specialized programming. ASICs like Google's TPU are customized for deep learning and provide high performance but limited flexibility. Emerging hardware aims to improve efficiency and better match neural network computations.
最新の HPC 技術を生かした AI・ビッグデータインフラの東工大 TSUBAME3.0 及び産総研 ABCINVIDIA Japan
- The document discusses the latest HPC technologies used in AI/Big Data infrastructures such as TSUBAME3.0 at Tokyo Institute of Technology and ABCI at AIST.
- It provides an overview of the capabilities and achievements of these supercomputers, including TSUBAME2.0 receiving the 2011 ACM Gordon Bell Prize.
- It emphasizes that future supercomputers need to focus on "BYTES" capabilities like bandwidth and capacity to better support large-scale data processing for AI/Big Data applications.
Breaking New Frontiers in Robotics and Edge Computing with AIDustin Franklin
This NVIDIA webinar will cover the latest tools and techniques to deploy advanced AI at the edge, including Jetson TX2 and TensorRT. Get up to speed on recent developments in robotics and deep learning.
By participating you'll learn:
1. How to build high-performance, energy-efficient embedded systems
2. Workflows for training AI in the cloud and deploying at the edge
3. The latest upcoming JetPack release and its performance improvements.
4. Real-time deep learning primitives for autonomous navigation.
5. NVIDIA’s latest Isaac Initiative for robotics
Devconf2017 - Can VMs networking benefit from DPDKMaxime Coquelin
DPDK brings high-performance/low-latency virtualization networking capabilities thanks to its Vhost/Virtio support. The session will first introduce DPDK and its Vhost/Virtio implementations, exposing to the audience examples of possible uses, and challenges that need to be addressed to achieve high-performance, functionality and reliability. Then, Vhost/Virtio improvements introduced in last DPDK release will be covered, such as receive path optimizations, Virtio's indirect descriptors support, or transmit zero copy to name a few. The speakers will explain which problems they aim to address, how they address them, mentioning their limitations.
Finally, the speakers, who are active DPDK's Virtio/Vhost contributors, will expose what new developments are in the pipe to tackle the remaining challenges.
The session will be presented so that DPDK developers and users find useful information on current developments and status. People not familiar with DPDK may find a overview, get and share ideas with other projects.
PgOpenCL is a new PostgreSQL procedural language that allows developers to write OpenCL kernels to harness the parallel processing power of GPUs. It introduces a new execution model where tables can be copied to arrays, passed to an OpenCL kernel for parallel operations on the GPU, and results copied back to tables. This unlock the potential for dramatically improved performance on compute-intensive database operations like joins, aggregations, and sorting.
In this deck from Switzerland HPC Conference, Gunter Roeth from NVIDIA presents: Deep Learning on the SaturnV Cluster.
"Machine Learning is among the most important developments in the history of computing. Deep learning is one of the fastest growing areas of machine learning and a hot topic in both academia and industry. It has dramatically improved the state-of-the-art in areas such as speech recognition, computer vision, predicting the activity of drug molecules, and many other machine learning tasks. The basic idea of deep learning is to automatically learn to represent data in multiple layers of increasing abstraction, thus helping to discover intricate structure in large datasets. NVIDIA has invested in SaturnV, a large GPU-accelerated cluster, (#28 on the November 2016 Top500 list) to support internal machine learning projects. After an introduction to deep learning on GPUs, we will address a selection of open questions programmers and users may face when using deep learning for their work on these clusters."
Watch the video: http://wp.me/p3RLHQ-gDv
Learn more: http://www.nvidia.com/object/dgx-saturnv.html
and
http://hpcadvisorycouncil.com/events/2017/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The RAPIDS suite of software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
2015年9月18日開催 GTC Japan 2015 講演資料
エヌビディア合同会社
エンタープライズプロダクト事業部 シニアソリューションアーキテクト Jeremy Main
A walk through of the techniques to monitor existing workstation workloads to create data-driven estimates of recommended user density levels based on the GPU requirements, frame buffer utilization and other factors as well as methods to confirm GPU resource utilization to ensure excellent performing NVIDIA GRID vGPU enabled virtual machines.
The document discusses NVIDIA data center GPUs such as the A100, A30, A40, and A10 and their performance capabilities. It provides examples of GPU accelerated application performance showing simulations in Simulia CST Studio, Altair CFD, and Rocky DEM achieving excellent speedups on GPUs. It also discusses Paraview visualization being accelerated with NVIDIA OptiX ray tracing, further sped up using RT cores. Looking ahead, the document outlines NVIDIA Grace CPUs which are designed to improve memory bandwidth between CPUs and GPUs for giant AI and HPC models.
The document discusses graphics processing units (GPUs) and general-purpose GPU (GPGPU) computing. It explains that GPUs were originally designed for computer graphics but can now be used for general computations through GPGPU. The document outlines CUDA and MPI frameworks for programming GPGPU applications and discusses how GPGPU provides highly parallel processing that is much faster than traditional CPUs. Example applications mentioned include molecular dynamics, bioinformatics, and high performance computing.
This document summarizes a presentation about Lagopus, an SDN software switch developed by NTT. Some key points:
- Lagopus aims to provide an SDN-aware switch software stack capable of 100Gbps performance, including an OpenFlow agent and extensible configuration data store.
- Existing virtual switches do not provide sufficient performance for carrier networks. Lagopus takes a simplified, modular design compiled using DPDK for high-performance packet processing.
- An FPGA-based 40GbE NIC was developed to offload processing tasks like encryption and packet scheduling for improved performance.
- Evaluation shows Lagopus can achieve wire-rate throughput of 10Gbps and support over 1 million flow
The document provides an overview of NVIDIA's professional VR solutions and technologies. It discusses the computing challenges of reproducing reality in VR/AR, including graphics/display, audio, touch/physics, and capturing 360 video. It highlights NVIDIA's VRWorks toolkit and Quadro VR solutions that help address these challenges. Key applications of professional VR discussed include design, manufacturing, medical, and collaboration workflows.
In this deck from the Argonne Training Program on Extreme-Scale Computing 2019, Howard Pritchard from LANL and Simon Hammond from Sandia present: NNSA Explorations: ARM for Supercomputing.
"The Arm-based Astra system at Sandia will be used by the National Nuclear Security Administration (NNSA) to run advanced modeling and simulation workloads for addressing areas such as national security, energy and science.
"By introducing Arm processors with the HPE Apollo 70, a purpose-built HPC architecture, we are bringing powerful elements, like optimal memory performance and greater density, to supercomputers that existing technologies in the market cannot match,” said Mike Vildibill, vice president, Advanced Technologies Group, HPE. “Sandia National Laboratories has been an active partner in leveraging our Arm-based platform since its early design, and featuring it in the deployment of the world’s largest Arm-based supercomputer, is a strategic investment for the DOE and the industry as a whole as we race toward achieving exascale computing.”
Watch the video: https://wp.me/p3RLHQ-l29
Learn more: https://insidehpc.com/2018/06/arm-goes-big-hpe-builds-petaflop-supercomputer-sandia/
and
https://extremecomputingtraining.anl.gov/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
GPU profiling for computer vision applicationsMai Nishimura
NVIDIA provides several tools for profiling GPU performance of computer vision applications, including nvprof, nvvp, and the next-generation Nsight Compute and Nsight Systems. Nvprof allows command-line profiling with different modes, while nvvp provides a GUI interface for visualizing profiling results. These tools help analyze kernel performance, identify bottlenecks like compute or memory limitations, and optimize applications. Tensorflow also includes a timeline tool for profiling graph execution.
GPGPU Accelerates PostgreSQL ~Unlock the power of multi-thousand cores~Kohei KaiGai
GPU processing provides significant performance gains for PostgreSQL according to benchmarks. PG-Strom is an open source project that allows PostgreSQL to leverage GPUs for processing queries. It generates CUDA code from SQL queries to accelerate operations like scans, joins, and aggregations by massive parallel processing on GPU cores. Performance tests show orders of magnitude faster response times for queries involving multiple joins and aggregations when using PG-Strom compared to the regular PostgreSQL query executor. Further development aims to support more data types and functions for GPU processing.
This presentation covers a talk on the topic of "AI on the edge". The talk was delivered in the Conference on Artificial Intelligence and Robotics Technology held on Jan 28, 2021 by National Center of Artificial Intelligence Pakistan & working group by Ministry of Science and Technology on AI & Robotics.
Clear Containers is an Open Containers Initiative (OCI) “runtime” that launches an Intel VT-x secured hypervisor rather than a standard Linux container. An introduction of Clear Containers will be provided, followed by an overview of CNM networking plugins which have been created to enhance network connectivity using Clear Containers. More specifically, we will show demonstrations of using VPP with DPDK and SRIO-v based networks to connect Clear Containers. Pending time we will provide and walk through a hands on example of using VPP with Clear Containers.
About the speaker: Manohar Castelino is a Principal Engineer for Intel’s Open Source Technology Center. Manohar has worked on networking, network management, network processors and virtualization for over 15 years. Manohar is currently an architect and developer with the ciao (clearlinux.org/ciao) and the clear containers (https://github.com/01org/cc-oci-runtime) projects focused on networking. Manohar has spoken at many Container Meetups and internal conferences.
Fugaku is a Japanese supercomputer utilizing Fujitsu's A64FX CPU. It was designed through an iterative co-design process between application developers and Fujitsu to achieve over 100x performance gain compared to the previous K computer within a 30-40MW power budget. The A64FX CPU utilizes 7nm technology and features 48 Arm-based cores with high bandwidth memory to achieve superior floating point and memory bandwidth performance efficiently. Early evaluations show Fugaku meeting performance and power targets and outperforming x86 processors for real applications.
最新の HPC 技術を生かした AI・ビッグデータインフラの東工大 TSUBAME3.0 及び産総研 ABCINVIDIA Japan
- The document discusses the latest HPC technologies used in AI/Big Data infrastructures such as TSUBAME3.0 at Tokyo Institute of Technology and ABCI at AIST.
- It provides an overview of the capabilities and achievements of these supercomputers, including TSUBAME2.0 receiving the 2011 ACM Gordon Bell Prize.
- It emphasizes that future supercomputers need to focus on "BYTES" capabilities like bandwidth and capacity to better support large-scale data processing for AI/Big Data applications.
Breaking New Frontiers in Robotics and Edge Computing with AIDustin Franklin
This NVIDIA webinar will cover the latest tools and techniques to deploy advanced AI at the edge, including Jetson TX2 and TensorRT. Get up to speed on recent developments in robotics and deep learning.
By participating you'll learn:
1. How to build high-performance, energy-efficient embedded systems
2. Workflows for training AI in the cloud and deploying at the edge
3. The latest upcoming JetPack release and its performance improvements.
4. Real-time deep learning primitives for autonomous navigation.
5. NVIDIA’s latest Isaac Initiative for robotics
Devconf2017 - Can VMs networking benefit from DPDKMaxime Coquelin
DPDK brings high-performance/low-latency virtualization networking capabilities thanks to its Vhost/Virtio support. The session will first introduce DPDK and its Vhost/Virtio implementations, exposing to the audience examples of possible uses, and challenges that need to be addressed to achieve high-performance, functionality and reliability. Then, Vhost/Virtio improvements introduced in last DPDK release will be covered, such as receive path optimizations, Virtio's indirect descriptors support, or transmit zero copy to name a few. The speakers will explain which problems they aim to address, how they address them, mentioning their limitations.
Finally, the speakers, who are active DPDK's Virtio/Vhost contributors, will expose what new developments are in the pipe to tackle the remaining challenges.
The session will be presented so that DPDK developers and users find useful information on current developments and status. People not familiar with DPDK may find a overview, get and share ideas with other projects.
PgOpenCL is a new PostgreSQL procedural language that allows developers to write OpenCL kernels to harness the parallel processing power of GPUs. It introduces a new execution model where tables can be copied to arrays, passed to an OpenCL kernel for parallel operations on the GPU, and results copied back to tables. This unlock the potential for dramatically improved performance on compute-intensive database operations like joins, aggregations, and sorting.
In this deck from Switzerland HPC Conference, Gunter Roeth from NVIDIA presents: Deep Learning on the SaturnV Cluster.
"Machine Learning is among the most important developments in the history of computing. Deep learning is one of the fastest growing areas of machine learning and a hot topic in both academia and industry. It has dramatically improved the state-of-the-art in areas such as speech recognition, computer vision, predicting the activity of drug molecules, and many other machine learning tasks. The basic idea of deep learning is to automatically learn to represent data in multiple layers of increasing abstraction, thus helping to discover intricate structure in large datasets. NVIDIA has invested in SaturnV, a large GPU-accelerated cluster, (#28 on the November 2016 Top500 list) to support internal machine learning projects. After an introduction to deep learning on GPUs, we will address a selection of open questions programmers and users may face when using deep learning for their work on these clusters."
Watch the video: http://wp.me/p3RLHQ-gDv
Learn more: http://www.nvidia.com/object/dgx-saturnv.html
and
http://hpcadvisorycouncil.com/events/2017/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The RAPIDS suite of software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
2015年9月18日開催 GTC Japan 2015 講演資料
エヌビディア合同会社
エンタープライズプロダクト事業部 シニアソリューションアーキテクト Jeremy Main
A walk through of the techniques to monitor existing workstation workloads to create data-driven estimates of recommended user density levels based on the GPU requirements, frame buffer utilization and other factors as well as methods to confirm GPU resource utilization to ensure excellent performing NVIDIA GRID vGPU enabled virtual machines.
The document discusses NVIDIA data center GPUs such as the A100, A30, A40, and A10 and their performance capabilities. It provides examples of GPU accelerated application performance showing simulations in Simulia CST Studio, Altair CFD, and Rocky DEM achieving excellent speedups on GPUs. It also discusses Paraview visualization being accelerated with NVIDIA OptiX ray tracing, further sped up using RT cores. Looking ahead, the document outlines NVIDIA Grace CPUs which are designed to improve memory bandwidth between CPUs and GPUs for giant AI and HPC models.
The document discusses graphics processing units (GPUs) and general-purpose GPU (GPGPU) computing. It explains that GPUs were originally designed for computer graphics but can now be used for general computations through GPGPU. The document outlines CUDA and MPI frameworks for programming GPGPU applications and discusses how GPGPU provides highly parallel processing that is much faster than traditional CPUs. Example applications mentioned include molecular dynamics, bioinformatics, and high performance computing.
This document summarizes a presentation about Lagopus, an SDN software switch developed by NTT. Some key points:
- Lagopus aims to provide an SDN-aware switch software stack capable of 100Gbps performance, including an OpenFlow agent and extensible configuration data store.
- Existing virtual switches do not provide sufficient performance for carrier networks. Lagopus takes a simplified, modular design compiled using DPDK for high-performance packet processing.
- An FPGA-based 40GbE NIC was developed to offload processing tasks like encryption and packet scheduling for improved performance.
- Evaluation shows Lagopus can achieve wire-rate throughput of 10Gbps and support over 1 million flow
The document provides an overview of NVIDIA's professional VR solutions and technologies. It discusses the computing challenges of reproducing reality in VR/AR, including graphics/display, audio, touch/physics, and capturing 360 video. It highlights NVIDIA's VRWorks toolkit and Quadro VR solutions that help address these challenges. Key applications of professional VR discussed include design, manufacturing, medical, and collaboration workflows.
In this deck from the Argonne Training Program on Extreme-Scale Computing 2019, Howard Pritchard from LANL and Simon Hammond from Sandia present: NNSA Explorations: ARM for Supercomputing.
"The Arm-based Astra system at Sandia will be used by the National Nuclear Security Administration (NNSA) to run advanced modeling and simulation workloads for addressing areas such as national security, energy and science.
"By introducing Arm processors with the HPE Apollo 70, a purpose-built HPC architecture, we are bringing powerful elements, like optimal memory performance and greater density, to supercomputers that existing technologies in the market cannot match,” said Mike Vildibill, vice president, Advanced Technologies Group, HPE. “Sandia National Laboratories has been an active partner in leveraging our Arm-based platform since its early design, and featuring it in the deployment of the world’s largest Arm-based supercomputer, is a strategic investment for the DOE and the industry as a whole as we race toward achieving exascale computing.”
Watch the video: https://wp.me/p3RLHQ-l29
Learn more: https://insidehpc.com/2018/06/arm-goes-big-hpe-builds-petaflop-supercomputer-sandia/
and
https://extremecomputingtraining.anl.gov/agenda-2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
GPU profiling for computer vision applicationsMai Nishimura
NVIDIA provides several tools for profiling GPU performance of computer vision applications, including nvprof, nvvp, and the next-generation Nsight Compute and Nsight Systems. Nvprof allows command-line profiling with different modes, while nvvp provides a GUI interface for visualizing profiling results. These tools help analyze kernel performance, identify bottlenecks like compute or memory limitations, and optimize applications. Tensorflow also includes a timeline tool for profiling graph execution.
GPGPU Accelerates PostgreSQL ~Unlock the power of multi-thousand cores~Kohei KaiGai
GPU processing provides significant performance gains for PostgreSQL according to benchmarks. PG-Strom is an open source project that allows PostgreSQL to leverage GPUs for processing queries. It generates CUDA code from SQL queries to accelerate operations like scans, joins, and aggregations by massive parallel processing on GPU cores. Performance tests show orders of magnitude faster response times for queries involving multiple joins and aggregations when using PG-Strom compared to the regular PostgreSQL query executor. Further development aims to support more data types and functions for GPU processing.
This presentation covers a talk on the topic of "AI on the edge". The talk was delivered in the Conference on Artificial Intelligence and Robotics Technology held on Jan 28, 2021 by National Center of Artificial Intelligence Pakistan & working group by Ministry of Science and Technology on AI & Robotics.
Clear Containers is an Open Containers Initiative (OCI) “runtime” that launches an Intel VT-x secured hypervisor rather than a standard Linux container. An introduction of Clear Containers will be provided, followed by an overview of CNM networking plugins which have been created to enhance network connectivity using Clear Containers. More specifically, we will show demonstrations of using VPP with DPDK and SRIO-v based networks to connect Clear Containers. Pending time we will provide and walk through a hands on example of using VPP with Clear Containers.
About the speaker: Manohar Castelino is a Principal Engineer for Intel’s Open Source Technology Center. Manohar has worked on networking, network management, network processors and virtualization for over 15 years. Manohar is currently an architect and developer with the ciao (clearlinux.org/ciao) and the clear containers (https://github.com/01org/cc-oci-runtime) projects focused on networking. Manohar has spoken at many Container Meetups and internal conferences.
Fugaku is a Japanese supercomputer utilizing Fujitsu's A64FX CPU. It was designed through an iterative co-design process between application developers and Fujitsu to achieve over 100x performance gain compared to the previous K computer within a 30-40MW power budget. The A64FX CPU utilizes 7nm technology and features 48 Arm-based cores with high bandwidth memory to achieve superior floating point and memory bandwidth performance efficiently. Early evaluations show Fugaku meeting performance and power targets and outperforming x86 processors for real applications.
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...ijdpsjournal
This paper studies the performance and energy consumption of several multi-core, multi-CPUs and manycore
hardware platforms and software stacks for parallel programming. It uses the Multimedia Multiscale
Parser (MMP), a computationally demanding image encoder application, which was ported to several
hardware and software parallel environments as a benchmark. Hardware-wise, the study assesses
NVIDIA's Jetson TK1 development board, the Raspberry Pi 2, and a dual Intel Xeon E5-2620/v2 server, as
well as NVIDIA's discrete GPUs GTX 680, Titan Black Edition and GTX 750 Ti. The assessed parallel
programming paradigms are OpenMP, Pthreads and CUDA, and a single-thread sequential version, all
running in a Linux environment. While the CUDA-based implementation delivered the fastest execution, the
Jetson TK1 proved to be the most energy efficient platform, regardless of the used parallel software stack.
Although it has the lowest power demand, the Raspberry Pi 2 energy efficiency is hindered by its lengthy
execution times, effectively consuming more energy than the Jetson TK1. Surprisingly, OpenMP delivered
twice the performance of the Pthreads-based implementation, proving the maturity of the tools and
libraries supporting OpenMP.
ASSESSING THE PERFORMANCE AND ENERGY USAGE OF MULTI-CPUS, MULTI-CORE AND MANY...ijdpsjournal
This paper studies the performance and energy consumption of several multi-core, multi-CPUs and manycore
hardware platforms and software stacks for parallel programming. It uses the Multimedia Multiscale
Parser (MMP), a computationally demanding image encoder application, which was ported to several
hardware and software parallel environments as a benchmark. Hardware-wise, the study assesses
NVIDIA's Jetson TK1 development board, the Raspberry Pi 2, and a dual Intel Xeon E5-2620/v2 server, as
well as NVIDIA's discrete GPUs GTX 680, Titan Black Edition and GTX 750 Ti. The assessed parallel
programming paradigms are OpenMP, Pthreads and CUDA, and a single-thread sequential version, all
running in a Linux environment. While the CUDA-based implementation delivered the fastest execution, the
Jetson TK1 proved to be the most energy efficient platform, regardless of the used parallel software stack.
Although it has the lowest power demand, the Raspberry Pi 2 energy efficiency is hindered by its lengthy
execution times, effectively consuming more energy than the Jetson TK1. Surprisingly, OpenMP delivered
twice the performance of the Pthreads-based implementation, proving the maturity of the tools and
libraries supporting OpenMP.
This presentation will cover the basics of performance testing. Configuring systems correctly is essential to characterizing the performance of SmartNICs. The configuration of BIOS, CPU allocation, OS and VM parameters will be covered. Also, choices of traffic generators and typical test topologies will be described.
A SURVEY ON GPU SYSTEM CONSIDERING ITS PERFORMANCE ON DIFFERENT APPLICATIONScseij
This document summarizes a survey on GPU systems and their performance on different applications. It discusses how GPUs can be used for general purpose computing due to their high parallel processing capabilities. Several computational intensive applications that achieve speedups when implemented on GPUs are described, including video decoding, matrix multiplication, parallel AES encryption, and password recovery for MS office documents. The GPU architecture and Nvidia's CUDA programming model are also summarized. While GPUs provide significant performance benefits, some limitations for non-graphics applications are noted. The conclusion is that GPUs are a good alternative for computational intensive tasks to reduce CPU load and improve performance compared to CPU-only implementations.
CGYRO Performance on Power9 CPUs and Volta GPUSIgor Sfiligoi
CGYRO, an Eulerian gyrokinetic solver for fusion plasma simulation has been ported and benchmarked on a Summit-like node, containing Power9 CPUs and Volta GPUs.
In this talk we present the porting experience and benchmark comparisons against nodes on other leadership systems.
Evolution of Supermicro GPU Server SolutionNVIDIA Taiwan
Supermicro provides energy efficient server solutions optimized for GPU computing. Their portfolio includes 1U and 4U servers that support up to 10 GPUs, delivering the highest rack-level and node-level GPU density. Their new generation of solutions are optimized for machine learning applications using NVIDIA Pascal GPUs, with features like NVLink for high bandwidth GPU interconnect and direct low latency data access between GPUs. These solutions deliver the highest performance per watt for parallel workloads like machine learning training.
Performance and power comparisons between nvidia and ati gpusijcsit
In recent years, modern graphics processing units have been widely adopted in high performance computing
areas to solve large scale computation problems. The leading GPU manufacturers Nvidia and ATI have
introduced series of products to the market. While sharing many similar design concepts, GPUs from these
two manufacturers differ in several aspects on processor cores and the memory subsystem. In this paper,
we choose two recently released products respectively from Nvidia and ATI and investigate the architectural
differences between them. Our results indicate that these two products have diverse advantages that
are reflected in their performance for different sets of applications. In addition, we also compare the energy
efficiencies of these two platforms since power/energy consumption is a major concern in the high
performance computing system.
The document discusses the evolution of GPU architecture and capabilities over time. It describes how GPUs have become massively parallel processors with programmable capabilities beyond just graphics. The document outlines the core components of a GPU including the graphics pipeline and programming model. It also discusses how GPUs are well suited for parallel, data-intensive applications and how their capabilities have expanded into general purpose computing through technologies like CUDA.
Revisiting Co-Processing for Hash Joins on the CoupledCpu-GPU Architecturemohamedragabslideshare
This document summarizes research on revisiting co-processing techniques for hash joins on coupled CPU-GPU architectures. It discusses three co-processing mechanisms: off-loading, data dividing, and pipelined execution. Off-loading involves assigning entire operators like joins to either the CPU or GPU. Data dividing partitions data between the processors. Pipelined execution aims to schedule workloads adaptively between the CPU and GPU to maximize efficiency on the coupled architecture. The researchers evaluate these approaches for hash join algorithms, which first partition, build hash tables, and probe tables on the input relations.
Exploring the Performance Impact of Virtualization on an HPC CloudRyousei Takano
The document evaluates the performance impact of virtualization on high-performance computing (HPC) clouds. Experiments were conducted on the AIST Super Green Cloud, a 155-node HPC cluster. Benchmark results show that while PCI passthrough mitigates I/O overhead, virtualization still incurs performance penalties for MPI collectives as node counts increase. Application benchmarks demonstrate overhead is limited to around 5%. The study concludes HPC clouds are promising due to utilization improvements from virtualization, but further optimization of virtual machine placement and pass-through technologies could help reduce overhead.
Penn State RCC has been a CUDA research center for the last year; this talk provides success stories and challenges. GPU case studies are given, including algorithm details and performance results.
GPU HPC Clusters document discusses GPU cluster research at NCSA including early GPU clusters like QP and Lincoln, follow-up clusters like AC that expanded GPU resources, and eco-friendly cluster EcoG. It describes ISL research in GPU and heterogeneous computing including systems software, runtimes, tools and application development.
The document provides an overview of big data analysis and parallel programming tools for R. It discusses what constitutes big data, popular big data applications, and relevant hardware and software. It then covers parallel programming challenges and approaches in R, including using multicore processors with the multicore package, SMP and cluster programming with foreach and doMC/doSNOW, NoSQL databases like Redis with doRedis, and job scheduling. The goal is to help users effectively analyze big data in R by leveraging parallelism.
This document summarizes VPU and GPGPU computing technologies. It discusses that a VPU is a visual processing unit, also known as a GPU. GPUs have massively parallel architectures that allow them to perform better than CPUs for some complex computational tasks. The document then discusses GPU, PPU and GPGPU architectures, programming models like CUDA, and applications of GPGPU computing such as machine learning, robotics and scientific research.
This document summarizes VPU and GPGPU technologies. It discusses that a VPU is a visual processing unit, also known as a GPU. GPUs have massively parallel architectures that allow them to perform better than CPUs for some complex computational tasks. The document then discusses GPU architecture including stream processing, graphics pipelines, shaders, and GPU clusters. It provides an example of using CUDA for GPU computing and discusses how GPUs are used for general purpose computing through frameworks like CUDA.
This document summarizes VPU and GPGPU computing technologies. It discusses that a VPU is a visual processing unit, also known as a GPU. GPUs provide massively parallel and multithreaded processing capabilities. GPUs are now commonly used for general purpose computing due to their ability to handle complex computational tasks faster than CPUs in some cases. The document then discusses GPU and PPU architectures, programming models like CUDA, and applications of GPGPU computing such as machine learning, robotics, and scientific research.
1) The document discusses implementing and evaluating deep neural networks (DNNs) on mainstream heterogeneous systems like CPUs, GPUs, and APUs.
2) Preliminary results show that an APU achieves the highest performance per watt compared to CPUs and GPUs for DNN models like MLP and autoencoders.
3) Data transfers between the CPU and GPU are identified as a bottleneck, but APUs can help avoid this issue through efficient data sharing and zero-copy techniques between the CPU and GPU.
NVIDIA GTC 2019: Red Hat and the NVIDIA DGX: Tried, Tested, TrustedJeremy Eder
Red Hat and NVIDIA collaborated to bring together two of the technology industry's most popular products: Red Hat Enterprise Linux 7 and the NVIDIA DGX system. This talk will cover how the combination of RHELs rock-solid stability with the incredible DGX hardware can deliver tremendous value to enterprise data scientists. We will also show how to leverage NVIDIA GPU Cloud container images with Kubernetes and RHEL to reap maximum benefits from this incredible hardware.
Similar to Can we boost more HPC performance? Integrate IBM POWER servers with GPUs to OpenStack Environment (20)
CloudNative Days Tokyo 2021で発表した資料です。
https://event.cloudnativedays.jp/cndt2021/talks/1279
Terraform、Pulumi、Kustomize、CrossplaneなどといったInfrastructure as Codeを取り巻くエコシステムを分析し、パブリッククラウドやKubernetesの力を最大限に引き出すためのツールスタックをどう組み上げていくか考察しています。
NTTコミュニケーションズでは、Azure Stack Hub with GPUを先行で導入し検証を行っています。本資料では、実際に利用している立場からデモを交えつつAzure Stack Hub with GPUのユースケースをお話すると共に、GPUのベンチマークを含む他社クラウドとの性能比較結果について情報共有をいたします。
This slide was for CLOUDEXPO 2017 in NYC. Consists of two part, One is for introducing existing WebRTC - IoT use cases. Another is conceptual consideration of Edge Computing scenario which leveraging WebRTC technology.
RabbitMQ is said a point of bottleneck in OpenStack.
We researched RabbitMQ and analyzed OpenStack RPC messaging.
This slide shows that RabbitMQ can scale out with HA setting.
WebRTC Conference Japan 2016 (2016年2月16日) の講演資料です。
発表者は中蔵聡哉と大津谷亮祐 http://www.slideshare.net/rotsuya です。
“Telexistence Robot controlled with WebRTC”
It's the presentation slides at WebRTC Conference Japan on Feb 16, 2016.
The presenters were Toshiya Nakakura and Ryosuke Otsuya http://www.slideshare.net/rotsuya .
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