Kepler vs Xeon Phi
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The document compares the Kepler GPU and Xeon Phi architectures through microbenchmarks that test memory-bound, compute-bound, and latency-bound workloads. It finds that for memory-bound workloads, vectorizing loads and using texture cache improves performance on Kepler, while gathering and aligned loads help Xeon Phi. For compute-bound workloads, vectorizing and using float4/double4 benefits Kepler, and intrinsics aid Xeon Phi. And for latency-bound workloads, loop interchange and skipping the L2 cache helps Kepler, while gathering and aligned loads assist Xeon Phi. The conclusion notes vendor performance data may differ from experiments and examples are available online.
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NERSC deployed the Cori system on over 9,000 Intel® Xeon Phi™ processors. This session describes the optimization strategy for porting codes that target traditional manycore architectures to the processors. We also discuss highlights and lessons learned from the optimization process on 20 applications associated with the NERSC Exascale Science Application Program (NESAP).
Harnessing OpenCL in Modern Coprocessors
Talk @ APT Group, University of Manchester, 06 August 2014
Abstract:
Nowadays HPC systems, such as those in the Top500, are equipped with a range of different processors, from multi-core CPUs to GPUs. Programming them can be a tough job, specially if we want to squeeze every last FLOPs of performance out of them.
As a Phd Student, I am now doing a brief research visit in the APT group, working in topics related to the programmability and efficient use of GPUs and many-core coprocessors. In particular, I am implementing a large database operation using OpenCL in these state-of-the-art systems. In this talk I will summarize my work in Manchester and discuss the future work in this topic.
Accessible hpc for everyone with docker and containers
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The document describes the QPACE supercomputer project which aims to build a supercomputer optimized for lattice QCD simulations using IBM PowerXCell 8i processors. Key aspects summarized are:
1) QPACE uses 256 node cards per rack, each with a PowerXCell 8i processor, to achieve 26 TFLOPS and 1 TB memory per rack.
2) Custom networks include a 3D torus for nearest neighbor communication and an interrupt tree for global operations.
3) The node card design features the PowerXCell processor, FPGA network processor, memory, and networking interfaces.
4) Early results found the hardware design worked well but network processor implementation and software deployment took longer than planned.
MattsonTutorialSC14.pptx
This document provides an agenda and overview for an introduction to OpenCL course. The agenda includes lectures on understanding host programs, kernel programs, memory models, and optimization. Course materials include OpenCL reference cards, specifications, and exercises. An introduction to OpenCL explains that it is an open standard for parallel programming across heterogeneous systems like CPUs and GPUs. The OpenCL platform model includes devices like GPUs that are divided into compute units and processing elements. Kernels define work-items that execute problems in parallel over a domain.
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)
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A Source-To-Source Approach to HPC Challenges
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Inferno Scalable Deep Learning on Spark
This document summarizes a presentation on Inferno, a system for scalable deep learning on Apache Spark. Inferno allows deep learning models built with Blaze, La Trobe University's deep learning system, to be trained faster using a Spark cluster. It coordinates distributed training of Blaze models across worker nodes, with optimized communication of weights and hyperparameters. Evaluation shows Inferno can train ResNet models on ImageNet up to 4-5 times faster than a single GPU. The presentation provides an overview of deep learning and Spark, demonstrates how Blaze allows easy model building, and explains Inferno's architecture for distributed deep learning training on Spark.
3. Synthesis.pptx
Synthesis in VLSI is the process of converting your code (program) into a circuit. In terms of logic gates, synthesis is the process of translating an abstract design into a properly implemented chip. Hardware Description Languages (HDLs) are specific programming languages that are used to explain the hardware of a circuit, and the computer subsequently builds the circuit depending on the programme you provided. A “Gate Level Netlist” is what you get once you finish synthesising. This is how your circuit will appear. It demonstrates how everything is interconnected. You can alter it if you like; the computer just synthesizes this netlist based on its best judgement. The synthesizer generates better netlists as the abilities improve and they become more proficient at creating HDL programmes.
Performance Optimization of SPH Algorithms for Multi/Many-Core Architectures
In the framework of the Intel Parallel Computing Centre at the Research Campus Garching in Munich, our group at LRZ presents recent results on performance optimization of Gadget-3, a widely used community code for computational astrophysics. We identify and isolate a sample code kernel, which is representative of a typical Smoothed Particle Hydrodynamics (SPH) algorithm and focus on threading parallelism optimization, change of the data layout into Structure of Arrays (SoA), compiler auto-vectorization and algorithmic improvements in the particle sorting. We measure lower execution time and improved threading scalability both on Intel Xeon (2.6× on Ivy Bridge) and Xeon Phi (13.7× on Knights Corner) systems. First tests on second generation Xeon Phi (Knights Landing) demonstrate the portability of the devised optimization solutions to upcoming architectures.
"Efficient Implementation of Convolutional Neural Networks using OpenCL on FP...
"Efficient Implementation of Convolutional Neural Networks using OpenCL on FP...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/altera/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Deshanand Singh, Director of Software Engineering at Altera, presents the "Efficient Implementation of Convolutional Neural Networks using OpenCL on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Convolutional neural networks (CNN) are becoming increasingly popular in embedded applications such as vision processing and automotive driver assistance systems. The structure of CNN systems is characterized by cascades of FIR filters and transcendental functions. FPGA technology offers a very efficient way of implementing these structures by allowing designers to build custom hardware datapaths that implement the CNN structure. One challenge of using FPGAs revolves around the design flow that has been traditionally centered around tedious hardware description languages.
In this talk, Deshanand gives a detailed explanation of how CNN algorithms can be expressed in OpenCL and compiled directly to FPGA hardware. He gives detail on code optimizations and provides comparisons with the efficiency of hand-coded implementations.開放運算&GPU技術研究班
This document introduces PyOpenCL and provides examples for using it to perform GPU computing via Python. It begins with an overview of GPU computing and OpenCL. It then discusses setting up the PyOpenCL environment on different platforms like Windows, MacOS, and Linux. Examples shown include printing "Hello Taiwan" on the GPU, performing arithmetic operations on arrays in parallel, and image processing tasks like grayscale conversion and blurring using OpenCL data types and memory models. Atomic functions and synchronization are also covered.
Understand and Harness the Capabilities of Intel® Xeon Phi™ Processors
The second-generation Intel® Xeon Phi™ processor offers new and enhanced features that provide significant performance gains in modernized code. For this lab, we pair these features with Intel® Software Development Products and methodologies to enable developers to gain insights on application behavior and to find opportunities to optimize parallelism, memory, and vectorization features.
SpeQuloS: A QoS Service for BoT Applications Using Best Effort Distributed Co...
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Simon Delamare Gilles Fedak Derrick Kondo Oleg Lodygensky
High-Performance Parallel and Distributed Computing, 2012
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Cost-effective software reliability through autonomic tuning of system resources
This document summarizes a seminar on achieving cost-effective software reliability through autonomic tuning of system resources. It discusses closed world systems which assume immutable environments and platforms. However, assumptions can clash with changing contexts over time. Open world software senses contexts and adapts assumptions. Examples given are adjusting redundancy and fault tolerance strategies like changing protocols or design patterns based on detected environmental conditions. Autonomic techniques like adaptive redundant data structures and normalized dissent in N-version programming are presented as ways to dynamically tune redundancy based on failure risk assessments. Simulations show such approaches improve reliability over static redundancy configurations.
On the Necessity and Inapplicability of Python
This document discusses the use of Python for numerical software development. It begins by introducing the author and their background in computational mechanics. It then discusses PyHUG, the Python user group in Taiwan, and PyCon Taiwan 2020.
The document notes that while Python is slow for number crunching, NumPy can provide reasonably fast performance. It explains that a hybrid architecture is commonly used, with the core computing kernel written in C++ for speed and Python used for the user-level API to describe complex problems more easily. An example of solving the Laplace equation is provided to demonstrate the speed differences between pure Python, NumPy, and C++ implementations.
The document advocates for training computer scientists in a hybrid approach through a numerical software
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Kepler vs Xeon Phi
- 1. Kepler vs Xeon Phi : our measures and their complete source code http://www.hpcmagazine.fr/en-couverture/kepler-vs-xeon-phi-nos-mesures/ Florent Duguet, PhD CEO - Altimesh http://www.altimesh.com/ ... article in French Presentation & translation by Ronan Keryell (SILKAN / Aptina)
- 3. Some functional analogies... ● Vendor data ● Flops/memop: minimal ratio to avoid waiting for memory
- 4. 3 microbenchmarks From theory to practice... ● 1 memory bound : read a vector – K20: Naïve/vectorized with float4/use texture cache – Phi : Naïve/vectorized/gather/aligned vector load ● 1 compute bound : Hörner approximation iterated (expm1())^12 (= 12 add, 24 mul, 60 madd) – K20: Naïve/vectorized with float4 or double4 – Phi : Naïve/intrinsics ● 1 latency bound : b[i] += a[i + index[k]] – K20: Naïve/loop interchange/ __ldg to skip L2$ – Phi : Naïve/vectorized/gather/aligned vector load
- 5. Memory bound
- 6. Memory bound
- 10. Latency bound
- 11. Conclusion ● (...) = (vendor data) ● Warning : in this experimentation fma counts for 1 FLOP instead of usual (... and constructors !) 2 FLOP ● Disclaimer : examples available :-) on http://www.hpcmagazine.fr/files/sources/003-Kepler-vs-Phi.zip