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Total Memory Encryption (TME) is a planned x86 instruction set extension proposed by Intel for a full physical memory encryption for DRAM and NVRAM with a single ephemeral key. TME can be further extended with the Multi-Key Total Memory Encryption (MKTME) extension which builds on TME and adds support multiple encryption keys.

Proposed by Intel in late 2017, TME and MKTME are a planned x86 instruction set extension that provides full physical memory encryption support for DRAM and NVRAM. TME is the base extension which adds the base capabilities for a single ephemeral key. MKTME is a further enhancement of TME that provides support for page-granular memory encryption through support for multiple encryption keys. Those capabilities are exposed to software via model-specific registers.

Multi-Key Total Memory Encryption (MKTME) is an extension of TME that adds support for multiple keys, therefore TME must be enabled in order to make use of MKTME. A fixed number of encryption keys are supported. Software can then configure the chip to use any subset of the available keys to encrypt any page of the memory. This functionality is available on a per-page basis.

Azure Virtual Desktop is a service that your users can deploy anytime, anywhere. That's why it's important that your users be able to customize which language their Windows 10 Enterprise multi-session image displays.

If you'd rather install languages through an automated process, you can set up a script in PowerShell. You can use the following script sample to install the Spanish (Spain), French (France), and Chinese (PRC) language packs and satellite packages for Windows 10 Enterprise multi-session, version 2004. The script integrates the language interface pack and all necessary satellite packages into the image. However, you can also modify this script to install other languages. Just make sure to run the script from an elevated PowerShell session, or else it won't work.

Scoring 774 points for the single-core benchmark, the Ryzen 7 chip is fractionally slower than your average Core i7-12700K/KF chip in the CPU-Z benchmark.. However, in the multi-threaded test, the AMD CPU was faster than the 12th Gen Core i7 K chips, beating them by about 7.5%. Compared to the Ryzen 7 5800X, the Ryzen 7000 series CPU was 21% faster in the single-core test and 28% faster in multi-core.

@Benchleaks also found a Geekbench entry where we see the same Ryzen chip, but this time paired with an Asus ROG Crosshair X670E Hero with 64GB of DDR5-6000 memory. Here, the CPU achieved a 2,209 single-core score and a 14,459 multi-core score. In this case, the Ryzen chip is faster than the 12700K in both cases, but the difference is just 2% for the multi-core benchmark. As for the single-core results, the difference increases to 16%.

But are we stuck with using Docker only on big and consuming x86 CPUs, letting behind a part of the IoT ecosystem? Luckily Docker handles multiple architectures natively.

Not that we could also have used multiple runners, each running on different hardware, and therefore we would have avoided cross-compilation. However, it requires much more effort in terms of infrastructure.

In this article, we demonstrated how Docker is able to build applications for several CPU architectures, which is essential for the support of the growing IoT ecosystem. While still experimental, Docker Buildx is a game-changer for delivering containers to multiple platforms, and already easily integrable in our CI/CD process.

Bonus: you can further optimize your Docker builds if the language you use for has good multi-architecture support (such as Java or Go). For example, you can build a Spring Boot application with a single platform compile:

In Real Mode, a segment and an offset register are used together to yield a final memory address. The value in the segment register is multiplied by 16 (shifted 4 bits to the left) and the offset is added to the result. This provides a usable address space of 1 MB. However, a quirk in the addressing scheme allows access past the 1 MB limit if a segment address of 0xFFFF (the highest possible) is used; on the 8086 and 8088, all accesses to this area wrapped around to the low end of memory, but on the 80286 and later, up to 65520 bytes past the 1 MB mark can be addressed this way if the A20 address line is enabled. See: The A20 Gate Saga.

>If you use an USB cable with Windows XP, a caution message "The software you are installing for this hardware has not passed Windows Logo testing to verify its compatibility with Windows XP." may appear, but you can continue the installation with no problem.If install the multiple models Multi-Function Station in one PC, please install by following steps.Execute the add model's Multi-Function Station installer program.Execute the add model's Multi-Function Station update installer program.After finish installation, "Select Device" button will be display and can be choose the model by Device List.

If you were writing an optimizing compiler/bytecode VM for a multicore CPU, what would you need to know specifically about, say, x86 to make it generate code that runs efficiently across all the cores?

This isn't a direct answer to the question, but it's an answer to a question that appears in the comments. Essentially, the question is what support the hardware gives to multi-core operation, the ability to run multiple software threads at truly the same time, without software context-switching between them. (Sometimes called an SMP system).

Nicholas Flynt had it right, at least regarding x86. In a multi-core environment (Hyper-threading, multi-core or multi-processor), the Bootstrap core (usually hardware-thread (aka logical core) 0 in core 0 in processor 0) starts up fetching code from address 0xfffffff0. All the other cores (hardware threads) start up in a special sleep state called Wait-for-SIPI. As part of its initialization, the primary core sends a special inter-processor-interrupt (IPI) over the APIC called a SIPI (Startup IPI) to each core that is in WFS. The SIPI contains the address from which that core should start fetching code.

The OS uses those to do the actual multi-threaded scheduling of software tasks. (A normal OS only has to bring up other cores once, at bootup, unless you're hot-plugging CPUs, e.g. in a virtual machine. This is separate from starting or migrating software threads onto those cores. Each core is running the kernel, which spends its time calling a sleep function to wait for an interrupt if there isn't anything else for it to be doing.)

As far as the actual assembly is concerned, as Nicholas wrote, there's no difference between the assemblies for a single threaded or multi threaded application. Each core has its own register set (execution context), so writing:

It is also possible to have non-preemptive multitasking systems, but those might require you to modify your code so that every threads yields (e.g. with a pthread_yield implementation), and it becomes harder to balance workloads.

Synchronization is done by the OS. Generally, each processor is running a different process for the OS, so the multi-threading functionality of the operating system is in charge of deciding which process gets to touch which memory, and what to do in the case of a memory collision.

For more information, see the Intel Multiprocessor Specification.Update: all the follow-up questions can be answered by just completely accepting that an n-way multicore CPU is almost1 exactly the same thing as n separate processors that just share the same memory.2 There was an important question not asked: how is a program written to run on more than one core for more performance? And the answer is: it is written using a thread library like Pthreads. Some thread libraries use "green threads" that are not visible to the OS, and those won't get separate cores, but as long as the thread library uses kernel thread features then your threaded program will automatically be multicore.1. For backwards compatibility, only the first core starts up at reset, and a few driver-type things need to be done to fire up the remaining ones.2. They also share all the peripherals, naturally.

You should learn about the facilities the OS (Linux or Windows or OSX) provides to allow you to run multiple threads. You should learn about parallelization APIs such as OpenMP and Threading Building Blocks, or OSX 10.6 "Snow Leopard"'s forthcoming "Grand Central".

You should consider if your compiler should be auto-parallelising, or if the author of the applications compiled by your compiler needs to add special syntax or API calls into his program to take advantage of the multiple cores.

This is a simplification but it gives you the basic idea of how it is done. More about multicores and multiprocessors on Embedded.com has lots of information about this topic ... This topic get complicated very quickly!

The assembly code will translate into machine code that will be executed on one core. If you want it to be multithreaded you will have to use operating system primitives to start this code on different processors several times or different pieces of code on different cores - each core will execute a separate thread. Each thread will only see one core it is currently executing on.

The main difference between a single- and a multi-threaded application is that the former has one stack and the latter has one for each thread. Code is generated somewhat differently since the compiler will assume that the data and stack segment registers (ds and ss) are not equal. This means that indirection through the ebp and esp registers that default to the ss register won't also default to ds (because ds!=ss). Conversely, indirection through the other registers which default to ds won't default to ss. 2b1af7f3a8