In my second blog post about the platform, I reconfigure the instruction set, crash Linux, debug the issue, and reconfigure the software to match the hardware.
For Simics training and demo purposes, we often use Linux* running on the virtual platforms. In the early days of Simics and embedded Linux, we built our own minimal configurations by hand to run on simple target systems. Most recently, we changed our Linux default demo and training setup to use Clear Linux*. This change showed us just how sophisticated modern Linux setups are – which is good in general, but it also can make some low-level details more complicated.
I wrote an Intel Developer Zone Blog Post about our experience moving to Clear Linux for Simics demos and training, which contains a lot more details of what we observed and did to make this work for our purposes.
I have just released a new blog post on my Intel Developer Zone blog, about how Simics runs
large huge workloads. I look back at the kinds of workloads that ran on Simics back in 1998 when the product first went commercial, and then look at some current examples running on Simics. This is the first post in a series intended to celebrate 20 years of Simics as a commercial product.
A while ago, I visited my Intel colleagues in Costa Rica and ran a workshop for university teachers and researchers, showing how Simics could be used in academia. I worked with a very smart and talented intern, Jose Fernando Molina, and after a rather long process I have published an interview with him on my Intel blog: https://software.intel.com/en-us/blogs/2017/12/05/windriver-simics-to-inspire-teachers-costarica
Over time, Intel and other processor core designers add more and more instructions to the cores in our machines. A good question is how quickly and easily new instructions added to an Instruction-Set Architecture (ISA) actually gets employed by software to improve performance and add new capabilities. Considering that our operating systems and programs are generally backwards-compatible, and run on all kind of hardware, can they actually take advantage of new instructions?
I love bug and debug stories in general. Bugs are a fun and interesting part of software engineering, programming, and systems development. Stories that involve running Simics on Simics to find bugs are a particular category that is fascinating, as it shows how to apply serious software technology to solve problems related to said serious software technology. On the Intel Software and Services blog, I just posted a story about just that: debugging a Linux kernel bug provoked by Simics, by running Simics on a small network of machines inside of Simics. See https://blogs.intel.com/evangelists/2016/05/30/finding-kernel-1-2-3-bug-running-wind-river-simics-simics/ for the full story.
There is a new post at my Wind River blog, about how I helped a colleague resolve a real problem using the preview version of the new Helix Lab Cloud system. The Lab Cloud right now is basically Simics behind a simplified web user interface, exposing the checkpointing and record-replay facilities in a very clear way. You can also share your sessions for live interactions with other people, which is truly cool.
There is an eternal debate going on in virtual platform land over what the right kind of abstraction is for each job. Depending on background, people favor different levels. For those with a hardware background, more details tend to be the comfort zone, while for those with a software background like myself, we are quite comfortable with less details. I recently did some experiments about the use of quite low levels of hardware modeling details for early architecture exploration and system specification.
A common question from simulation users to us simulation providers is “can I simulate a machine with N cores”, where N is “large”. As if running lots of cores was a simulation system or even a hardware problem. In almost all cases, the problem is with software. Creating an arbitrary configuration in a virtual platform is easy. Creating a software stack for that arbitrary platform is a lot harder, since an SMP software stack needs to understand about the cores and how they communicate.
Essentially, what you need is a hardware design that has addressing room for lots of cores, and a software stack that is capable of using lots of cores — even if such configurations do not exist in hardware. Unfortunately, since software is normally written to run on real existing machines, there tends to be unexpected limitations even where scalability should be feasible “in principle”.
Here is the story of how I convinced Linux to handle more than two cores in a virtual MPC8641D machine.
This is a small Linux SMP programming tip, which I had a hard time finding documented clearly anywhere on the web. I guess people won’t find it here either, but with some luck some search engine will pick up on this.
Edited on 2009-Feb-01, to include the link to the illustrated guide that really helps you get there faster. Thanks Simon! Also, promoted to front page, original post was put up on 2008-Nov-09.
Thanks to Simon Kågströms post (and the even better second-generation with screenshots) about using Eclipse for the Linux kernel, I have a much nicer work environment now for my ongoing work in learning Linux device drivers on PowerPC, which has helped me work my way through several hard-to-figure-out system calls. Continue reading “Eclipse Linux Kernel Indexing Works”
To continue from last week’s post about my Linux device driver and hardware teaching setup in Simics, here is a lesson I learnt this week when doing some performance analysis based on various hardware speeds.
There are times when working with virtual hardware and not real hardware feels very liberating and efficient (not to mention safe). Bringing up, modifying, and extending operating systems is one obvious such case. Recently, I have been preparing an open-source-based demonstration and education systems based on embedded PowerPC machines, and teaching myself how to do Linux device drivers in the process. This really brought out the best in virtual platform use.
A small tidbit that I found interesting due to the targeted platform. LinuxDevices reports that the VirtualLogix VLX-NI virtualization layer that used to run only on x86 platforms now also run on TI DSPs in the C64+ series. Basically, you put their virtualization layer on the DSP, and you can then on the same core run both a Linux kernel and a DSP/BIOS kernel. Thus supporting traditional DSP development and Linux-style development on the same core.