Ken describes how he injected a virus into a compiler. Not only did his compiler know it was compiling the login function and inject a backdoor, but it also knew when it was compiling itself and injected the backdoor generator into the compiler it was creating. The source code for the compiler thereafter contains no evidence of either virus.
Ken wrote, In demonstrating the possibility of this kind of attack, I picked on the C compiler. I could have picked on any program-handling program such as an assembler, a loader, or even hardware microcode. As the level of program gets lower, these bugs will be harder and harder to detect. A well installed microcode bug will be almost impossible to detect.
Source: The Ken Thompson Hack
The moral is obvious. You can’t trust code that you did not totally create yourself. (Especially code from companies that employ people like me.) No amount of source-level verification or scrutiny will protect you from using untrusted code. In demonstrating the possibility of this kind of attack, I picked on the C compiler. I could have picked on any program-handling program such as an assembler, a loader, or even hardware microcode. As the level of program gets lower, these bugs will be harder and harder to detect. A well installed microcode bug will be almost impossible to detect.
via ACM Classic: Reflections on Trusting Trust.
Programs don’t magically become faster when they are run on GPUs. E.g. Linear Algebra algorithms work really well on CPUs, but if ported 1 to 1 ( as this would ) to a GPU their performance is just abysmal. Usually one needs to use a specially designed algorithm that can actually use the massive parallelism of a GPU and not get stuck e.g. trying to synchronize or doing other kinds of communication. GPUs really like doing the same operation on independent data, which is basically what happens when rendering an image, they are not really designed to have operations that need information of all other data, or neighbouring data in a grid…. . Just because something works on a GPU does not mean its efficient, thus the performance could be much worse using a GPU .
Also balancing CPU and GPU usage is even harder ( maybe impossible ? ) as you cannot predict what kind of System you will run your software on, thus usually these days the CPU feeds the GPU with data ( with the current Tesla cards only 1 core per GPU, this changes in the Kepler version to 32 ) and does some processing that can’t be done on the GPU, but do not share any kind of workloads.
I don’t know how the h.264 codec is structured or if it is possible to have performance gains on encoding. However I really doubt that x.264 can be just ported as they rely heavily on CPU specific features ( SSE etc ) which is quite different to the much higher level bytecode that Java would produce.
via Rootbeer GPU Compiler Lets Almost Any Java Code Run On the GPU – Slashdot.
This is a list of some compiler error messages, a few run-time errors and a few hints that can be used to resolve them.
via Typical C or C++ Error Messages or Problems.
This is a very useful reference.
Going back to 2009, FreeBSD developers have worked to replace GCC with LLVM/Clang. Over time, there’s been numerous improvements especially with the FreeBSD 9.0 release. The FreeBSD developers are interested in doing away with the GPL-licensed GNU Compiler Collection and instead use the Apple-sponsored LLVM/Clang work that’s under a BSD-like license.
via [Phoronix] FreeBSD 10 To Use Clang Compiler, Deprecate GCC.