[matthijs/projects/internship.git] / Report / Main / Problems / Challenges.tex

\section{Challenges and Solutions}
This section will describe the challenges faced during the work I have performed
and the solutions found for both the task itself and the challenges.

\subsection{What is MontiumC?}
A critical question popped up at the beginning of my internship: What is
MontiumC? Previously, there was no real specification of MontiumC. There was
documentation about the functions that could be used, some examples and a lot of
personal knowledge in the heads of the Recore employees, but ultimately MontiumC
was ``whatever the compiler eats''.

To be able to create a proper set of transformations, the constraints on the
input and output of that transformation process should be properly specified.
This entails two parts: Specifying the MontiumC language, and specifying the
Montium IR constraints, which is is the input to the backend.

Specifying Montium IR was relatively easy, since it is defined directly by the
backend. The MontiumC specification is slightly more complex. There are two
different angles to it: What does the compiler support, and what do we want the
compiler to support.

\subsubsection{What is supported?}
One angle for looking at MontiumC is seeing what the compiler can currently
compile and turn that into a formal specification. This approach works fine to
set a baseline for the compiler: A point to start improving the transformations
from. Apart from that, this initial specification is also useful in reviewing
existing MontiumC code and verifying existing transformations.

Existing MontiumC code is of course supported by the compiler (since it has been
tweaked until it worked). However, the existing code might not fully work within
the produced specification. This can happen in particular when existing code
makes use of corner cases in the compiler that have been missed in (or left out
of) the specification, because they will not work reliably in all cases.

The best way to detect these cases is making the compiler check its input using
an the specification. This way, any code operating outside of the specification
can be detected automatically. Writing such checks has not happened yet, mainly
because the impact of the new hardware on MontiumC is not quite clear yet.

Existing transformations, on the other hand, might miss a few corner cases. When
writing the specification, a particular feature might appear supported by the
compiler. On closer inspection, the transformation passes might not do
the right thing when faced with particular corner
cases, which either need to be fixed or put into the specification.

The best way to detect these cases is writing a lot of structured testing code,
which uses (combinations of) the features that are supported according to the
specification. This way, corner cases exposed by the testing code can be
detected automatically. A framework for this testing has been set up and
partially filled with small tests.

Building this initial specification did pose a number of challenges. Since
simply trying all possible C features to see if they are accepted by the
MontiumC compiler and thus valid MontiumC is a lengthy process and only useful
in a limited way. A more constructive way would be to examine the compiler
components to see what transformations are applied and from that derive the
specification for valid MontiumC. However, most of these components are not very
transparent. The Clang frontend supports a lot of C constructs and is not a
trivial piece of code. It also has support for Objective C and partially C++,
which makes it harder to see which code paths are actually used for compiling
MontiumC. This issue is only partially solved, meaning that there might still be
incorrect or missing cases in the specification.

Another problem is the complexity of the C language. Although individual
features can be described and supported with relative ease, combining features
can easily lead to complex constructs which are hard to transform into supported
Montium IR. This became more of an issue after adding features to the base
specification of MontiumC, since the base specification only supports a
few C features.

\subsubsection{What is wanted?}
A completely different angle of looking at this is from the requirements point
of view. What do we want MontiumC to support? This angle is even harder than the
previous one, since there are a lot of levels of requirements. Ideally, MontiumC
would not exist and our compiler would support the C language fully. However,
this would require a very complicated compiler (both frontend and backend).

Not only that, it is simply not possible to map all valid C programs to the
Montium hardware. "Regular" architectures don't suffer from this problem (as
much), since most instruction sets are not fundamentally different from the
features supported by C (or other imperative languages). This means that
anything is mappable, but with a simple compiler this will not result in the
most efficient code. In the Montium case, a lot of things simply cannot be
mapped on the hardware at all.

Considering that our ideal is not reachable (by far), every feature
considered for MontiumC was evaluated thoroughly for feasibility, both in hardware
and in the compiler. In practice, this meant that new language features would be
informally expressed and discussed, and only added to the specification after
being succesfully implemented. This is conforming to the incremental development
of MontiumC that was envisioned at the outset of its development.

To get a feeling for what MontiumC looks like, consider the fragment in
figure \ref{ExampleLow}. This is a piece of code that reads values from
one memory, multiplies them by two, and writes them back to another
memory. As you can see, this is an awful lot of code. This has two main
reasons. First, looping and memory indexing is very explicit and takes a
lot of instructions. Second, the code is effectively software pipelined
to make it run more efficiently.

In figure \ref{ExampleHigh} the same code is displayed, but this time
using higer level C features (for loops, arra indexing). This is the
level of code we are trying to achieve, but we're not there yet. It
should be noted that this is still not "normal" C, since we use the
"imul" function instead of the normal * operator. However, since the
Montium defines a lot of different operations, most of which have a
number of options (saturation, truncation, post shifting, etc.) these
cannot all be mapped onto normal C operators. By using a specific
function call for each, we can still distinguish between all the
different operations and add extra arguments where needed.

\begin{figure}
        \caption{Low level MontiumC example}
\label{ExampleLow}
\begin{verbatim}
mem input;
mem output;
word factor;

void run(void) {
  factor = from_int(2);
  input  = alloc_mem(P0M0);
  output = alloc_mem(P0M1);
  set_base(input, 0);
  set_offset(input, 0);
  set_base(output, -1);
  set_offset(output, -1);

  next_cycle();
  word in = read_mem(input);
  word out = p0o0(imul(ra1(in), rc1(factor)))
  add_offset(input, 1);
  add_offset(output, 1);
  init_loop(LC1, 8);
  do {
    write_mem(output, out);
    in = read_mem(input);
    out = p0m0(imul(ra1(in), rc1(factor)))
    add_offset(input, 1);
    add_offset(output, 1);
  } while(loop_next(LC1));

  write_mem(output, out);
\end{verbatim}
\end{figure}

\begin{figure}
        \caption{High level MontiumC example}
        \label{ExampleHigh}
\begin{verbatim}
P0M0 int input[10];
P0M1 int output[10];

void run(void) {
  for (int i=0; i<10; ++i)
    output[i] = mul(input[i], 2);
}
\end{verbatim}
\end{figure}

\subsection{Familiarizing with LLVM}
Since the frontend heavily relies on the LLVM project for doing it's work, one
of the first challenges was to get myself familiar with LLVM. There were two main
aspects to this: Getting to know my way around the LLVM codebase and getting to
know the LLVM community.

LLVM has a pretty large amount of documentation, I spent most of my first
weeks with reading tutorials and documents. Since there was already a (very
preliminary) version of the clang-based frontend, I also had some code to play
with.

During this period, it was not completely clear what the frontend should
be doing and what transformations should be written. To prevent circular
dependencies in my tasks (I can't write any code before I know what needs to be
written, but I can't really tell what's needed until I know how the code works,
which I can't effectively learn without actively working with it, etc.) I
started out with adapting the loop unrolling pass in LLVM to be better suited to
the Montium architecture. Eventually, this code didn't turn out to be
immediately useful because deciding when to unroll a loop and when not to turned
out rather hard (it's still not included currently). Working with this pass did
prove very insightful, however, as to how the LLVM framework is built and what its
possibilities are.

Additionally, during my working with the code in this internship I also produced
a number of patches for LLVM, containing bugfixes, some cleanup and
documentation improvements. Since the best way to integrate with any open source
project seems to be contributing code, I was giving commit access to the LLVM
tree not long thereafter. This access has proved very useful during the rest of
the internship, since it was now a a lot easier to make (simple) changes to the
LLVM framework to better suit the needs of Recore.

A major challenge during my internship was to find the balance between doing
work specifically for Recore, and doing work that is useful for LLVM in general.
Any changes that are directly required by the Montium frontend and the LLVM changes they
need are obvious. However, usually when making changes to the main LLVM
tree, just changing enough for Recore is not engough for LLVM. Since the LLVM
code must work on any program, not just MontiumC programs, extra changes are
required (see also parapgrah \ref{StayingGeneric}). This is also an issue of
building up credit within the LLVM community: The more you contribute to LLVM,
the more influence you have when things need changing. 

Lastly, this is also a matter of efficiency: If I have been working
with a particular piece of code intensively, it is more efficient for me to fix
a bug in that code than most others, even though the particular bug does not
interfere with the MontiumC frontend. In the end, I think I managed to find a
decent balance, though it might have been tipped slighly in favour of the LLVM
project.

\subsection{Working together}
Since the compiler plays a fairly central role in the development process at
Recore, I have been cooperating with a number of different people, in different
areas. On one end, the compiler is directly used by the DSP engineers, so a lot
of the requirements and wishes for the compiler come from them. Also, they are
often providing bug reports and other feedback, which ensures regular contact.

On the other end, I have been in contact with the developer of the backend very
intensively, since most changes made to either component needed changes in the
other one as well. Compiler changes also require hardware support, so working
with the hardware developers was not uncommon either. In practice, most
communication with the hardware developers went through the backend
developer, except for the design discussion concerning the new Montium
hardware design (also see section \ref{Pipelining} below).

In addition, discussions regarding design issues at various levels often happen
out in the open, which invites people with an opinion about something to
chime in, without the overhead of planning a seperate meeting for each
topic. This allows for very quick feedback on ideas from people in all areas
in an efficient way.

\subsection{Staying generic}
\label{StayingGeneric}
The toughest challenge by far was to fit the features and changes required by
Recore into code that is maintained by the LLVM project. The main problem here
is a clash of goals: The LLVM project aims for code that performs well for their
supported architectures, while Recore aims for code that performs well (or more
often, can compile at all) for the Montium architecture.

In general, these problems mainly occur because MontiumC and in particular
MontiumIR poses a number of constraints that are not present in other
architectures. This means that some transformations present in LLVM will violate
these constraints (which would result in better code on most other
architectures) resulting in miscompiled or uncompilable code.

In some cases, these extra constraints can be formulated in a generic way, such
that the LLVM code can handle architectures with or without the constraint.
In a lot of cases, however, the constraint is so Montium specific that changing
LLVM to support it is not feasible. In a few cases, this meant that the backend
was changed to support more features of the LLVM IR. In other cases, a
Recore-specific transformations was added to solve these problems. In a few more
cases, the problems are still unresolved, effectively resulting in additional
constraints on the MontiumC language.

\subsection{New hardware design}
\label{Pipelining}
I've also been involved for a bit with the instruction scheduling algorithm
required for the new (pipelined) hardware design and the hardware design itself.
Even though this is completely outside of the area of my assignment, the initial
prototype of that scheduler was created by someone else using LLVM. Because of
my experience with LLVM, I have been assisting him with that.  Initially mostly
helping out with hints on LLVM coding, but later also with thinking about the
scheduler and hardware design.

I will not go into much detail about the new hardware and its scheduler here,
but I will highlight the most important challenges and tradeoffs.

\subsubsection{Tradeoffs}
In general, the most important tradeoff seems to be between flexibility and
everything else (code size, performance, complexity, hardware area). This
flexibility is defined in terms of possible instructions, present
connections, register files sizes, etc.

An important reason to be flexible is for programmability. If the hardware is
regular, making a compiler that produces optimal code gets a lot easier.
On the other hand, the compiler also limits flexibility. If the hardware
has flexibility that the compiler will never use, it's better to save
area and complexity by making the hardware less flexible. Exactly for this
reason, it is important to develop hardware and supporting software in parallel,
instead of using the hardware first, software later approach used with the
initial Montium. This allows for a much better balanced and usable design,
without any unused extras.

\subsubsection{Inner loop pipelining}
When trying to improve runtime performance, the main focus is on
optimizing loops, and inner loops (loops that contain no other loops) in
particular. Since inner loops are executed the most often (compared to
other loops and code), it is the most
efficient to optimize inner loops. Also, inner loops can most optimally
use the parellel processing power of the Montium, because they can be
software pipelined. 

Software pipelining means that the compiler will emit code that performs
operations that belong in different iterations of the original loop during the
same cycle. Since data dependencies within a loop body usually severely limit
the amount of operations that can be done in parallel, pipelining allows the
second (and further) iteration to start well before the first iteration is done.
This is done by dividing the loop body in a number of stages, that would
normally be executed sequentially. These stages are then executed in parallel,
but for different iterations (ie, run stage 2 of iteration i, while running
stage 1 of iteration i+1).

This approach allows a loop to be ran a lot faster than executing a
single iteration at a time. However, since the instructions for the
first and last few iterations (the prologue and epilogue) are distinctly
different from the loop "kernel", the number of instructions needed for
a pipelined loop can easily increase a lot.

However, all pipelined loops share a very distinct structure (first
stage 1, then stage 1+2, then stage 1+2+3, etc, then all stages at the
same time, similar for the epilogue). Also, every instruction in the
prologue and epilogue are a strict subset of the instructions in the
kernel. By adding some hardware support for exactly this structure, the
code size increase for the prologue and epilogue can be effectively
reduced to a fixed number of instructions (which take the number of stages as a
parameter and uses preloaded instructions with explicit stage annotation).

The tradeoff here is that this hardware is only usable specifically for these
inner loops, any other code will leave this extra hardware unused. However,
since pipelined inner loops appear to be very common, this should not be a
problem at all.

\subsubsection{Code compression}
Another very important tradeoff concerns codesize. In the old hardware, a lot of
flexibility in the original code was achieved by using inline functions (since
the hardware has only very limited support for code reuse through function
calls). This resulted in a lot of code duplication, which was compensated for by
using two level configuration registers to be able to reuse (partial)
instructions nonetheless (which will still need a lot of sequencer instructions,
but those will be a lot smaller than the full instruction).

On the new hardware, however, function calls are more powerful, which should
lead to a lot less code duplication. For this reason, putting every instruction
in configuration registers might actually take more space instead of less. It
should be noted that, the configuration registers of the old Montium are
effectively a compiler controlled cache that is mandatory and static
(instructions must be in the cache and the cache cannot be modified at runtime).
By lifting these limitations, we get a cache that is a lot more flexible.
However, since it is compiler controlled (as opposed to letting the hardware
decide what ends up in the cache), the performance of the code remains very
predictable (unlike traditional caches). Additionally, this has the advantage
that the size of the cache is no longer an upper bound on the program size, but
only the instruction memory is (which can be a lot bigger).

The tradeoff here is that the sequencer instructions will get a lot bigger,
since they need to contain a full instruction word (which would be preloaded
into the CRs in the old design) which can be up to a few hundred bits long.
Since not all sequencer instructions need to be this long (executing out of the
cache should be a lot shorter), different instruction lengths should be
supported. Also, some smart compression techniques should also be applied to
those long instruction words, though that can only be done once there is some
more application code to gather statistics about.