How To Install and Boot Microsoft Hyper-V 2019 from Persistent Memory (or not)

How to
June 17, 2021

In a previous post I described how to install and boot Fedora Linux using only Persistent Memory, no SSDs are required. For this follow on post, I attempted to install Microsoft Windows Server 2022 onto the persistent memory.

TL;DR - I was able to select the PMem devices as the install disk, but when the installer begins to write data, we get an “Error code: 0xC0000005”. I haven’t found a solution to this problem (yet).

Create a Bootable USB

Follow the instructions in my previous blog where I document How to Create a Bootable Windows USB in Fedora Linux .

Install Hyper-V 2019

The first setup screen you’ll see when booting from the ISO or USB image allows you to select the installation language, Time and keyboard layout. Click “Next“ once you have confirmed your choices.

Click “Install Now” to begin the installation process

Read and accept the EULA License terms

Select ‘Custom: Install the newer version of Hyper-V Server only (advanced)’

Select a disk or partition to install Windows Server, you can optionally create a new one from the available capacity or use all the available capacity by clicking “Next“. I found the 60GB PMem Devices listed as ‘Drive 6’ and ‘Drive 7’. Unfortunately, there’s no way to obtain information about the device, so you have to identify using the capacity.

Shortly into the installation, I encountered error code 0xC0000005 - “Windows installation encountered an unexpected error. Verify that the installation sources are accessible, and restart the installation.”

Most search results for this error during install indicate “The error halts the Windows OS installation and is mostly related to temporary hardware issues with the RAM or corrupt hard drive due to bad sectors.” I know the RAM and PMem is good, and I verified the USB image installs to an SSD without any issues.

I encountered the same problem while installing Windows Server 2019 and 2022 . If I get some free cycles, I’ll continue to debug the problem. If you have any suggestions, please leave me a comment.

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An Introduction to Generative Prompt Engineeering

Introduction

Over the past few years, there has been a significant explosion in the use and development of large language models (LLMs). An LLM is a language model consisting of a neural network with many parameters (commonly multi-billions of weights), trained on large quantities of text. Some of the most popular large language models are: GPT-3 (Generative Pretrained Transformer 3) – developed by OpenAI ; BERT (Bidirectional Encoder Representations from Transformers) – developed by Google; RoBERTa (Robustly Optimized BERT Approach) – developed by Facebook AI; T5 (Text-to-Text Transfer Transformer) – developed by Google. Many others exist and continue to emerge. These language models are designed to understand and generate natural language text, allowing for a wide range of applications such as chatbots, content creation, language translation, and more.

How To Install and Boot Microsoft Windows Server 2019 from Persistent Memory

How to

How to Confirm Virtual to Physical Memory Mappings for PMem and FSDAX Files

Are you curious whether your application’s memory-mapped files are really using Intel Optane Persistent Memory (PMem), Compute Express Link (CXL) Non-Volatile Memory Modules (NV-CMM), or another DAX-enabled persistent memory device? Want to understand how virtual memory maps onto physical, non-volatile regions? Let’s use easily adaptable scripts in both Python and C to confirm this on your Linux system, definitively.

Why Does This Matter?

With the advent of persistent memory and DAX (Direct Access) filesystems, applications can memory-map files directly onto PMem, bypassing the traditional DRAM page cache. This promises significant performance and durability improvements for data-intensive workloads and databases, such as SQLite, Redis, and others.