The document discusses three tips for optimizing C++ code: measure performance to guide optimizations, reduce computational strength by using simpler operations like comparisons over divisions, and minimize writes to arrays which can disable optimizations. Introducing extra passes or copying data can improve performance by reducing writes and enabling compiler optimizations.
東大電子情報工学科の田浦先生におよるオペーレーティングシステム授業で行なった「仮想マシンにおけるメモリ管理及びExtended Page Table」についての発表のスライドです。
発表で取り扱ったのはMerrifiel(2017)の"Performance Implications of Extended Page Tables on Virtualized x86 Processors"です。
The document discusses implementing PCIe Address Translation Services (ATS) in ARM-based systems-on-chips (SoCs). It describes an example ARM server system with various components like CPUs, memory controllers, and I/O devices. It then explains how ATS works to improve memory access performance by allowing devices to cache address translations locally instead of relying solely on the IOMMU. The document outlines the typical components involved in ATS like the address translation cache, translating agent, and address translation protection table. It also describes how the ARM System MMU (SMMU) implements ATS and supports distributed address translation caching by endpoints.
The document discusses three tips for optimizing C++ code: measure performance to guide optimizations, reduce computational strength by using simpler operations like comparisons over divisions, and minimize writes to arrays which can disable optimizations. Introducing extra passes or copying data can improve performance by reducing writes and enabling compiler optimizations.
東大電子情報工学科の田浦先生におよるオペーレーティングシステム授業で行なった「仮想マシンにおけるメモリ管理及びExtended Page Table」についての発表のスライドです。
発表で取り扱ったのはMerrifiel(2017)の"Performance Implications of Extended Page Tables on Virtualized x86 Processors"です。
The document discusses implementing PCIe Address Translation Services (ATS) in ARM-based systems-on-chips (SoCs). It describes an example ARM server system with various components like CPUs, memory controllers, and I/O devices. It then explains how ATS works to improve memory access performance by allowing devices to cache address translations locally instead of relying solely on the IOMMU. The document outlines the typical components involved in ATS like the address translation cache, translating agent, and address translation protection table. It also describes how the ARM System MMU (SMMU) implements ATS and supports distributed address translation caching by endpoints.
1. The document discusses dictionary-based text mining using examples such as ML-Ask, J-LIWC, and J-MFD.
2. Dictionary-based text mining involves mapping text to conceptual dictionaries or psychological categories using specialized dictionaries. Examples provided include sentiment analysis using ML-Ask and analyzing text based on LIWC and MFD categories.
3. Challenges with dictionary-based approaches include limited coverage of dictionaries and lack of frequent updates, though results can be easily interpreted.
The document provides an overview of the Advanced Encryption Standard (AES) encryption algorithm in Japanese. It discusses AES being selected as the encryption standard by NIST in 1997. It then explains the basic processing steps of AES including AddRoundKey, SubBytes, ShiftRows, and MixColumns. It notes AES uses a block size of 128 bits and has key sizes of 128, 192, and 256 bits, corresponding to 10, 12, and 14 rounds respectively. It also discusses AES being used for Wi-Fi encryption through the WPA2 protocol.
This document discusses optimizations for deep learning frameworks on Intel CPUs and Fugaku processors. It introduces oneDNN, an Intel performance library for deep neural networks. JIT assembly using Xbyak is proposed to generate optimized code depending on parameters at runtime. Xbyak has been extended to AArch64 as Xbyak_aarch64 to support Fugaku. AVX-512 SIMD instructions are briefly explained.
RoCEv2 is an extension of the original RoCE specification announced in 2010 that brought the benefits of Remote Direct Memory Access (RDMA) I/O architecture to Ethernet-based networks. RoCEv2 addresses the needs of today’s evolving enterprise data centers by enabling routing across Layer 3 networks. Extending RoCE to allow Layer 3 routing provides better traffic isolation and enables hyperscale data center deployments.
Watch the video presentation: http://insidehpc.com/2014/09/slidecast-ibta-releases-updated-specification-rocev2/
Arm DynamIQ: Intelligent Solutions Using Cluster Based MultiprocessingArm
DynamIQ is Arm's new cluster-based multiprocessing architecture that allows for heterogeneous processing. It features the DynamIQ Shared Unit that connects CPU cores and manages shared resources like caches. The new Cortex-A75 and Cortex-A55 CPU cores are the first built on DynamIQ. Cortex-A75 provides a significant performance boost while Cortex-A55 improves efficiency. Together they enable scalable solutions from edge to cloud.
1. The document discusses dictionary-based text mining using examples such as ML-Ask, J-LIWC, and J-MFD.
2. Dictionary-based text mining involves mapping text to conceptual dictionaries or psychological categories using specialized dictionaries. Examples provided include sentiment analysis using ML-Ask and analyzing text based on LIWC and MFD categories.
3. Challenges with dictionary-based approaches include limited coverage of dictionaries and lack of frequent updates, though results can be easily interpreted.
The document provides an overview of the Advanced Encryption Standard (AES) encryption algorithm in Japanese. It discusses AES being selected as the encryption standard by NIST in 1997. It then explains the basic processing steps of AES including AddRoundKey, SubBytes, ShiftRows, and MixColumns. It notes AES uses a block size of 128 bits and has key sizes of 128, 192, and 256 bits, corresponding to 10, 12, and 14 rounds respectively. It also discusses AES being used for Wi-Fi encryption through the WPA2 protocol.
This document discusses optimizations for deep learning frameworks on Intel CPUs and Fugaku processors. It introduces oneDNN, an Intel performance library for deep neural networks. JIT assembly using Xbyak is proposed to generate optimized code depending on parameters at runtime. Xbyak has been extended to AArch64 as Xbyak_aarch64 to support Fugaku. AVX-512 SIMD instructions are briefly explained.
RoCEv2 is an extension of the original RoCE specification announced in 2010 that brought the benefits of Remote Direct Memory Access (RDMA) I/O architecture to Ethernet-based networks. RoCEv2 addresses the needs of today’s evolving enterprise data centers by enabling routing across Layer 3 networks. Extending RoCE to allow Layer 3 routing provides better traffic isolation and enables hyperscale data center deployments.
Watch the video presentation: http://insidehpc.com/2014/09/slidecast-ibta-releases-updated-specification-rocev2/
Arm DynamIQ: Intelligent Solutions Using Cluster Based MultiprocessingArm
DynamIQ is Arm's new cluster-based multiprocessing architecture that allows for heterogeneous processing. It features the DynamIQ Shared Unit that connects CPU cores and manages shared resources like caches. The new Cortex-A75 and Cortex-A55 CPU cores are the first built on DynamIQ. Cortex-A75 provides a significant performance boost while Cortex-A55 improves efficiency. Together they enable scalable solutions from edge to cloud.
5. 2019/11/09 5
Kirchhoff's Flow Law (KFL) / Potential Law (KPL)
Verilog-AMS Language Reference Manual Version 2.4.0 , May 30, 2014
Verilog-AMS
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Spice/AMS vs RNM 比較
DVCLUB Europe: Sept 2017; Real Value Modeling for Improving the Verification Performance, Mallikarjuna Reddy(T&V), Venkatramanarao (Mindlance Tech)
https://www.testandverification.com/conferences/dvclub/europe/sep2017/dvclub-real-value-modeling-improving-verification-performance
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SystemVerilog : User-Defined Net-type
● 6.5 Nets and variables
– There are two main groups of data objects: variables and nets.
● 6.6 Net types
– There are two different kinds of net types: built-in and user-defined.
– built-in net types → Table 6-1
● resolving multiple drivers → Table 6-2
IEEE Std 1800-2017 “6. Data types”