today's howtos

• Bash Beginner Series #3: Passing Arguments to Bash Scripts

• How to enable SSHD on FreeBSD server / jail

• How to Upgrade to Linux Mint 20 Ulyana

• How to Install MySQL 8 on Ubuntu 16.10 Linux Operating System

• Install VirtualBox 6.1 Guest Additions (Linux VM) on Oracle Linux 8

• Export a Linux VM guest using VirtualBox 6.1 on Oracle Linux 8

• Create a Linux VM using VirtualBox 6.1 on Oracle Linux 8

NanoPi NEO3 Headless SBC Launched for $20 and up

Last month, we found out FriendlyELEC was working on NanoPi NEO3, a tiny SBC powered by Rockchip RK3328 processor and made for headless applications and networked storage thanks to Gigabit Ethernet and USB 3.0 ports, as well as a 26-pin GPIO header. At the time, the board was still been finalized, but the company has now started to take orders for $20 and up depending on options which include a cute white enclosure... [...] The Wiki has been updated as well, and the company provides both Ubuntu Core 18.04 based FriendlyCore, and OpenWrt based FriendlyWrt operating systems for the board with both relying on Linux 5.4.12 kernel. I’d also expect Armbian to eventually provide Ubuntu 20.04 and Debian 10 images.

Moving (parts of) the Cling REPL in Clang

Motivation
===

Over the last decade we have developed an interactive, interpretative 
C++ (aka REPL) as part of the high-energy physics (HEP) data analysis 
project -- ROOT [1-2]. We invested a significant  effort to replace the 
CINT C++ interpreter with a newly implemented REPL based on llvm -- 
cling [3]. The cling infrastructure is a core component of the data 
analysis framework of ROOT and runs in production for approximately 5 
years.

Cling is also  a standalone tool, which has a growing community outside 
of our field. Cling’s user community includes users in finance, biology 
and in a few companies with proprietary software. For example, there is 
a xeus-cling jupyter kernel [4]. One of the major challenges we face to 
foster that community is  our cling-related patches in llvm and clang 
forks. The benefits of using the LLVM community standards for code 
reviews, release cycles and integration has been mentioned a number of 
times by our "external" users.

Last year we were awarded an NSF grant to improve cling's sustainability 
and make it a standalone tool. We thank the LLVM Foundation Board for 
supporting us with a non-binding letter of collaboration which was 
essential for getting this grant.


Background
===

Cling is a C++ interpreter built on top of clang and llvm. In a 
nutshell, it uses clang's incremental compilation facilities to process 
code chunk-by-chunk by assuming an ever-growing translation unit [5]. 
Then code is lowered into llvm IR and run by the llvm jit. Cling has 
implemented some language "extensions" such as execution statements on 
the global scope and error recovery. Cling is in the core of HEP -- it 
is heavily used during data analysis of exabytes of particle physics 
data coming from the Large Hadron Collider (LHC) and other particle 
physics experiments.


Plans
===

The project foresees three main directions -- move parts of cling 
upstream along with the clang and llvm features that enable them; extend 
and generalize the language interoperability layer around cling; and 
extend and generalize the OpenCL/CUDA support in cling. We are at the 
early stages of the project and this email intends to be an RFC for the 
first part -- upstreaming parts of cling. Please do share your thoughts 
on the rest, too.


Moving Parts of Cling Upstream
---

Over the years we have slowly moved some patches upstream. However we 
still have around 100 patches in the clang fork. Most of them are in the 
context of extending the incremental compilation support for clang. The 
incremental compilation poses some challenges in the clang 
infrastructure. For example, we need to tune CodeGen to work with 
multiple llvm::Module instances, and finalize per each 
end-of-translation unit (we have multiple of them). Other changes 
include small adjustments in the FileManager's caching mechanism, and 
bug fixes in the SourceManager (code which can be reached mostly from 
within our setup). One conclusion we can draw from our research is that 
the clang infrastructure fits amazingly well to something which was not 
its main use case. The grand total of our diffs against clang-9 is: `62 
files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently 
being upgraded from llvm-5 to llvm-9.

A major weakness of cling's infrastructure is that it does not work with 
the clang Action infrastructure due to the lack of an 
IncrementalAction.  A possible way forward would be to implement a 
clang::IncrementalAction as a starting point. This way we should be able 
to reduce the amount of setup necessary to use the incremental 
infrastructure in clang. However, this will be a bit of a testing 
challenge -- cling lives downstream and some of the new code may be 
impossible to pick straight away and use. Building a mainline example 
tool such as clang-repl which gives us a way to test that incremental 
case or repurpose the already existing clang-interpreter may  be able to 
address the issue. The major risk of the task is avoiding code in the 
clang mainline which is untested by its HEP production environment.
There are several other types of patches to the ROOT fork of Clang, 
including ones  in the context of performance,towards  C++ modules 
support (D41416), and storage (does not have a patch yet but has an open 
projects entry and somebody working on it). These patches can be 
considered in parallel independently on the rest.

Extend and Generalize the Language Interoperability Layer Around Cling
---

HEP has extensive experience with on-demand python interoperability 
using cppyy[6], which is built around the type information provided by 
cling. Unlike tools with custom parsers such as swig and sip and tools 
built on top of C-APIs such as boost.python and pybind11, cling can 
provide information about memory management patterns (eg refcounting) 
and instantiate templates on the fly.We feel that functionality may not 
be of general interest to the llvm community but we will prepare another 
RFC and send it here later on to gather feedback.


Extend and Generalize the OpenCL/CUDA Support in Cling
---

Cling can incrementally compile CUDA code [7-8] allowing easier set up 
and enabling some interesting use cases. There are a number of planned 
improvements including talking to HIP [9] and SYCL to support more 
hardware architectures.



The primary focus of our work is to upstreaming functionality required 
to build an incremental compiler and rework cling build against vanilla 
clang and llvm. The last two points are to give the scope of the work 
which we will be doing the next 2-3 years. We will send here RFCs for 
both of them to trigger technical discussion if there is interest in 
pursuing this direction.


Collaboration
===

Open source development nowadays relies on reviewers. LLVM is no 
different and we will probably disturb a good number of people in the 
community ;)We would like to invite anybody interested in joining our 
incremental C++ activities to our open every second week calls. 
Announcements will be done via google group: compiler-research-announce 
(https://groups.google.com/g/compiler-research-announce).



Many thanks!


David & Vassil