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amx-guide

Advanced Matrix Extensions (AMX) Guide
https://github.com/mikeroyal/amx-guide

Last synced: 17 days ago
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C/C++ Learning Resources

C/C++ Tools and Frameworks

  • AWS SDK for C++
  • Azure SDK for C++
  • Azure SDK for C
  • C++ Client Libraries for Google Cloud Services
  • Visual Studio - rich application that can be used for many aspects of software development. Visual Studio makes it easy to edit, debug, build, and publish your app. By using Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, and Windows Store.
  • Vcpkg
  • ReSharper C++
  • AppCode - fixes to resolve them automatically. AppCode provides lots of code inspections for Objective-C, Swift, C/C++, and a number of code inspections for other supported languages. All code inspections are run on the fly.
  • CLion - platform IDE for C and C++ developers developed by JetBrains.
  • Code::Blocks
  • CppSharp
  • Conan
  • High Performance Computing (HPC) SDK
  • Thrust - level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies such as CUDA, TBB, and OpenMP integrates with existing software.
  • Boost - edge C++. Boost has been a participant in the annual Google Summer of Code since 2007, in which students develop their skills by working on Boost Library development.
  • Automake
  • Cmake - source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.
  • GDB
  • GCC - C, Fortran, Ada, Go, and D, as well as libraries for these languages.
  • GSL - squares fitting. There are over 1000 functions in total with an extensive test suite.
  • OpenGL Extension Wrangler Library (GLEW) - platform open-source C/C++ extension loading library. GLEW provides efficient run-time mechanisms for determining which OpenGL extensions are supported on the target platform.
  • Libtool
  • Maven
  • TAU (Tuning And Analysis Utilities) - based sampling. All C++ language features are supported including templates and namespaces.
  • OpenCV - time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
  • ANTLR (ANother Tool for Language Recognition)
  • Oat++ - efficient web application. It's zero-dependency and easy-portable.
  • JavaCPP
  • Cython
  • Spdlog - only/compiled, C++ logging library.
  • Infer - C, and C. Infer is written in OCaml.

Computational Linear Algebra

ii. Systems of equations as matrix equations

i. Solving systems of equations

Computing the Inverse of a Matrix

iii. Transpose of a Matrix

ii. Systems of equations as matrix equations

ii. Using elementary matrices

i. Using row operations

Contribute

viii. Linear Regression

CUDA Learning Resources

viii. Linear Regression

CUDA Tools Libraries, and Frameworks

viii. Linear Regression

  • CUDA Toolkit - accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.
  • CUDA-X HPC - X HPC includes highly tuned kernels essential for high-performance computing (HPC).
  • CUTLASS - performance matrix-multiplication (GEMM) at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS.
  • CUB
  • Tensorman
  • CuPy - compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.
  • CatBoost
  • cuDF - like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
  • ArrayFire - purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.
  • AresDB - powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.
  • Arraymancer - dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.
  • Kintinuous - time dense visual SLAM system capable of producing high quality globally consistent point and mesh reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor.
  • Fuzzy logic - tree processing and better integration with rules-based programming.
  • ResearchGate
  • Support Vector Machine (SVM) - group classification problems.
  • OpenClipArt
  • Convolutional Neural Networks (R-CNN)
  • CS231n
  • Slideteam
  • Multilayer Perceptrons (MLPs) - layer neural networks composed of multiple layers of perceptrons with a threshold activation.
  • wikimedia
  • Decision trees - structured models for classification and regression.
  • CMU
  • Naive Bayes - theorem.html) with strong independence assumptions between the features.
  • mathisfun
  • GraphVite - speed and large-scale embedding learning in various applications.

Deep Learning Learning Resources

viii. Linear Regression

Deep Learning Tools, Libraries, and Frameworks

viii. Linear Regression

  • AMD FidelityFX Super Resolution (FSR) - quality solution for producing high resolution frames from lower resolution inputs. It uses a collection of cutting-edge Deep Learning algorithms with a particular emphasis on creating high-quality edges, giving large performance improvements compared to rendering at native resolution directly. FSR enables “practical performance” for costly render operations, such as hardware ray tracing for the AMD RDNA™ and AMD RDNA™ 2 architectures.
  • Intel Xe Super Sampling (XeSS) - cores to run XeSS. The GPUs will have Xe Matrix eXtenstions matrix (XMX) engines for hardware-accelerated AI processing. XeSS will be able to run on devices without XMX, including integrated graphics, though, the performance of XeSS will be lower on non-Intel graphics cards because it will be powered by DP4a instruction.
  • Deep Learning Toolbox™ - term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
  • Reinforcement Learning Toolbox™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
  • Deep Learning HDL Toolbox™ - built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
  • LIBSVM - SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
  • Microsoft AirSim - source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
  • CARLA - source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.
  • ROS/ROS2 bridge for CARLA(package) - way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.
  • ROS Toolbox
  • Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
  • Image Processing Toolbox™ - standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
  • Computer Vision Toolbox™
  • Model Predictive Control Toolbox™ - loop simulations, you can evaluate controller performance.
  • Predictive Maintenance Toolbox™ - based and model-based techniques, including statistical, spectral, and time-series analysis.
  • Vision HDL Toolbox™ - streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
  • Automated Driving Toolbox™ - eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
  • UAV Toolbox
  • Navigation Toolbox™ - based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
  • Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
  • Mapping Toolbox™

Defintions

iii. Matrix-vector product

ii. Matrix operations

i. Vector operations

iv. Linear transformations

v. Fundamental vector spaces

FPGA Learning Resources

FPGA Tools

  • Apio - built packages, project configuration tools and easy command interface to verify, synthesize, simulate and upload your verilog designs.
  • IceStorm
  • Icestudio
  • FuseSoC - winning package manager and a set of build tools for HDL (Hardware Description Language) code and FPGA/ASIC development.
  • OpenWiFi - source IEEE802.11/Wi-Fi baseband chip/FPGA design.
  • PipeCNN - based FPGA Accelerator for Large-Scale Convolutional Neural Networks (CNNs). Currently, there is a growing trend among developers in the FPGA community to utilize High Level Synthesis (HLS) tools to design and implement customized circuits on FPGAs.
  • Verilog to Routing(VTR) - source framework for conducting FPGA architecture and CAD Research & Development. The VTR design flow takes as input a Verilog description of a digital circuit, and a description of the target FPGA architecture.
  • PlatformIO - in. It provides support for multiplatforms and frameworks such as IoT, Arduino, CMSIS, ESP-IDF, FreeRTOS, libOpenCM3, mbed OS, Pulp OS, SPL, STM32Cube, Zephyr RTOS, ARM, AVR, Espressif (ESP8266/ESP32), FPGA, MCS-51 (8051), MSP430, Nordic (nRF51/nRF52), NXP i.MX RT, PIC32, RISC-V.
  • PlatformIO for VSCode
  • OpenTimer - Performance Timing Analysis Tool for VLSI Systems.
  • Chipyard - based systems-on-chip. It will allow you to leverage the Chisel HDL, Rocket Chip SoC generator, and other Berkeley projects to produce a RISC-V SoC with everything from MMIO-mapped peripherals to custom accelerators.
  • The Eclipse Embedded CDT - ins for Arm & RISC-V C/C++ developers.
  • Unicorn - platform, multi-architecture CPU emulator framework(ARM, AArch64, M68K, Mips, Sparc, X86) based on QEMU.
  • Keystone - platform, multi-architecture(Arm, Arm64, Hexagon, Mips, PowerPC, Sparc, SystemZ & X86) assembler framework.
  • Reko
  • Renode
  • Diosix - metal hypervisor written in Rust for RISC-V.
  • Tock - M and RISC-V based embedded platforms. Tock's design centers around protection, both from potentially malicious applications and from device drivers.
  • LLVM - end(parser and lexer) and a back-end (code that converts LLVM's representation to actual machine code).

Learning Resources for ML

viii. Linear Regression

Linear Algebra Learning Resources