source: anr/section-3.1.tex @ 151

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Our project covers several critical domains in system design in order
to achieve high performance computing. Starting from a high level description we aim 
at generating automatically both hardware and software components of the system.

\subsubsection{High Performance Computing}
% Un marché bouffé par les archi GPGPU tel que le FERMI de NvidiaCUDA programming language
High-Performance Computing (HPC) world is composed of three main families of architectures:
many-core, GPGPU (General Purpose computation on Graphics Unit Processing) and FPGA.
The two first families are dominating the market by taking benefit 
of the strength and influence of mass-market leaders (Intel, Nvidia).
%such as Intel for many-core CPU and Nvidia for GPGPU.
In this market, FPGA architectures are emerging and very promising.
By adapting architecture to the software, % (the opposite is done in the others families)
FPGAs architectures enable better performance
(typically between x10 and x100 accelerations)
while using smaller size and less energy (and heat).
However, using FPGAs presents significant challenges~\cite{hpc06a}.
First, the operating frequency of an FPGA is low compared to a high-end microprocessor.
Second, based on Amdahl law,  HPC/FPGA application performance is unusually sensitive 
to the implementation quality~\cite{hpc06b}.
% Thus, the performance strongly relies on the detected parallelism.
% (pour résumer les 2 derniers points)
Finally, efficient design methodology are required in order to
hide FPGA complexity and the underlying implantation subtleties to HPC users,
so that they don't have to change their habits and can have equivalent design productivity
than in others families~\cite{hpc07a}. 

%état de l'art FPGA 
HPC/FPGA hardware is only now emerging and in early commercial stages, 
but these techniques have not yet caught up. 
Industrial (Mitrionics~\cite{hpc08}, Gidel~\cite{hpc09}, Convey Computer~\cite{hpc10}) and academic (CHREC)
researches on HPC-FPGA are mainly conducted in the USA. 
None of the approaches developed in these researches are fulfilling entirely the
challenges described above. For example, Convey Computer proposes application-specific instruction set extension of x86 cores in FPGA accelerator,
but extension generation is not automated and requires hardware design skills.
Mitrionics has an elegant solution based on a compute engine specifically
developed for high-performance execution in FPGAs. Unfortunately, the design flow
is based on a new programming language (mitrionC) implying designer efforts and poor portability.
% tool relying on operator libraries (XtremeData),  
% Parle t-on de l'OPenFPGA consortium, dont le but est : "to accelerate the incorporation of reconfigurable computing technology in high-performance and enterprise applications" ?

Thus, much effort is required to develop design tools that translate high level
language programs to FPGA configurations.
Moreover, as already remarked in~\cite{hpc11}, Dynamic Partial Reconfiguration~\cite{hpc12}
(DPR, which enables changing a part of the FPGA, while the rest is still working)
appears very interesting for improving HPC performance as well as reducing required area.

\subsubsection{System Synthesis}
Today, several solutions for system design are proposed and commercialized.
The existing commercial or free tools does not
cover the whole system synthesis process in a full automatic way. Moreover,
they are bound to a particular device family and to IPs library.
The most commonly used are provided by \altera and \xilinx to promote their
FPGA devices. These two representative tools used to synthesize SoC on FPGA
are introduced below.
\\
The \xilinx System Generator for DSP~\cite{system-generateur-for-dsp} is a
plug-in to Simulink that enables designers to develop high-performance DSP
systems for \xilinx FPGAs.
Designers can design and simulate a system using MATLAB and Simulink. The
tool will then automatically generate synthesizable Hardware Description
Language (HDL) code mapped to \xilinx pre-optimized algorithms.
However, this tool targets only DSP based algorithms, \xilinx FPGAs and
cannot handle a complete SoC. Thus, it is not really a system synthesis tool.
\\
In the opposite, SOPC Builder~\cite{spoc-builder} allows to describe a
system, to synthesis it, to programm it into a target FPGA and to upload a
software application.
% FIXME(C2H from \altera, marche vite mais ressource monstrueuse)
Nevertheless, SOPC Builder does not provide any facilities to synthesize
coprocessors. System Designer must provide the synthesizable description
with the feasible bus interface. Design Space Exploration is thus limited
and SystemC simulation is not possible neither at transactional nor at Cycle
accurate level. 
\\
In addition, \xilinx System Generator and SOPC Builder are closed world
since each one imposes their own IPs which are not interchangeable.

\subsubsection{High Level Synthesis}
High Level Synthesis translates a sequential algorithmic description and a
set of constraints (area, power, frequency, ...) to a micro-architecture at
Register Transfer Level (RTL).
Several academic and commercial tools are today available. Most common
tools are SPARK~\cite{spark04}, GAUT~\cite{gaut08}, UGH~\cite{ugh08} in the
academic world and CATAPULTC~\cite{catapult-c}, PICO~\cite{pico} and
CYNTHETIZER~\cite{cynthetizer} in commercial world.  Despite their
maturity, their usage is restrained by:
\begin{itemize}
\item The HLS tools are not integrated into an architecture and system exploration tool.
Thus, a designer who needs to accelerate a software part of the system, must adapt it manually 
to the HLS input dialect and performs engineering work to exploit the synthesis result 
at the system level.
\item HLS tools take into account only one or few constraints simultaneously while realistic
designs are multi-constrained. 
Moreover, low power consumption constraint is mandatory for embedded systems. 
However, it is not yet well handled or not handle at all by the synthesis tools already available.
\item The parallelism is extracted from initial algorithmic specification.
To get more parallelism or to reduce the amount of required memory in the SoC, the user
must re-write the algorithmic specification while there is techniques as polyedric
transformations to increase the intrinsic parallelism.
\item While they support limited loop transformations like loop unrolling and loop
pipelining, current HLS tools do not provide support for design space exploration neither
through automatic loop transformations nor through memory mapping.
\item Despite they have the same input language (C/C++), they are sensitive to the style in
which the algorithm is written. Consequently, engineering work is required to swap from 
a tool to another.
\item They do not respect accurately the frequency constraint when they target an FPGA device.
Their error is about 10 percent. This is annoying when the generated component is integrated
in a SoC since it will slow down the hole system.
\end{itemize}
Regarding these limitations, it is necessary to create a new tool generation reducing the gap 
between the specification of an heterogeneous system and its hardware implementation.
\mustbecompleted {FIXME :: Ajouter ref livre + D\&T}

\subsubsection{Application Specific Instruction Processors}

ASIP (Application-Specific Instruction-Set Processor) are programmable
processors in which both the instruction and the micro architecture have
been tailored to a given application domain (e.g. video processing), or to a
specific application.  This specialization usually offers a good compromise
between performance (w.r.t a pure software implementation on an embedded
CPU) and flexibility (w.r.t an application specific hardware co-processor).
In spite of their obvious advantages, using/designing ASIPs remains a
difficult task, since it involves designing both a micro-architecture and a
compiler for this architecture. Besides, to our knowledge, there is still
no available open-source design flow for ASIP design even if such a tool
 would be valuable in the
context of a System Level design exploration tool.
\par
In this context, ASIP design based on Instruction Set Extensions (ISEs) has 
received a lot of interest~\cite{NIOS2}, as it makes micro architecture synthesis 
more tractable \footnote{ISEs rely on a template micro-architecture in which 
only a small fraction of the architecture has to be specialized}, and help ASIP
designers to focus on compilers, for which there are still many open
problems\cite{ARC08}.
This approach however has a strong weakness, since it also significantly reduces 
opportunities for achieving good seedups (most speedup remain between 1.5x and 
2.5x), since ISEs performance is generally tied down by I/O constraints as 
they generally rely on the main CPU register file to access data.

% (
%automaticcaly extraction ISE candidates for application code \cite{CODES04}, 
%performing efficient instruction selection and/or storage resource (register) 
%allocation \cite{FPGA08}).  
To cope with this issue, recent approaches~\cite{DAC09,CODES08,TVLSI06} advocate the use of 
micro-architectural ISE models in which the coupling between the processor micro-architecture
and the ISE component is thightened up so as to allow the ISE to overcome the register 
I/O limitations, however these approaches generally tackle the problem for a compiler/simulation 
point of view and not address the problem of generating synthesizable representations for 
these models. 

We therefore strongly believe that there is a need for an open-framework which
would allow researchers and system designers to :
\begin{itemize}
\item Explore the various level of interactions between the original CPU micro-architecure
and its extension (for example throught a Domain Specific Language targeted at micro-architecture
specification and synthesis).
\item Retarget the compiler instruction-selection (or prototype nex passes) passes so as
to be able to take advantage of this ISEs.
\item Provide  a complete System-level Integration for using ASIP as SoC building blocks 
(integration with application specific blocks, MPSoc, etc.)
\end{itemize}

\subsubsection{Automatic Parallelization}
% FIXME:LIP FIXME:PF FIXME:CA
% Paul je ne suis pas sur que ce soit vraiment un etat de l'art
% Christophe, ce que tu m'avais envoye se trouve dans obsolete/body.tex
%\mustbecompleted{
%Hardware is inherently parallel. On the other hand, high level languages, 
%like C or Fortran, are abstractions of the processors of the 1970s, and
%hence are sequential. One of the aims of an HLS tool is therefore to
%extract hidden parallelism from the source program, and to infer enough
%hardware operators for its efficient exploitation.
%\\
%Present day HLS tools search for parallelism in linear pieces of code
%acting only on scalars -- the so-called basic blocs. On the other hand,
%it is well known that most programs, especially in the fields of signal
%processing and image processing, spend most of their time executing loops
%acting on arrays. Efficient use of the large amount of hardware available
%in the next generation of FPGA chips necessitates parallelism far beyond
%what can be extracted from basic blocs only.

%The Compsys team of LIP has built an automatic parallelizer, Syntol, which
%handle restricted C programs -- the well known polyhedral model --, 
%computes dependences and build a symbolic schedule. The schedule is
%a specification for a parallel program. The parallelism itself can be 
%expressed in several ways: as a system of threads, or as data-parallel
%operations, or as a pipeline. In the context of the COACH project, one
%of the task will be to decide which form of parallelism is best suited
%to hardware, and how to convey the results of Syntol to the actual
%synthesis tools. One of the advantages of this approach is that the
%resulting degree of parallelism can be easilly controlled, e.g. by 
%adjusting the number of threads, as a mean of exploring the 
%area / performance tradeoff of the resulting design.

%Another point is that potentially parallel programs necessarily involve
%arrays: two operations which write to the same location must be executed
%in sequence. In synthesis, arrays translate to memory. However, in FPGAs,
%the amount of on-chip memory is limited, and access to an external memory
%has a high time penalty. Hence the importance of reducing the size of
%temporary arrays to the minimum necessary to support the requested degree
%of parallelism. Compsys has developped a stand-alone tool, Bee, based
%on research by A. Darte, F. Baray and C. Alias, which can be extended
%into a memory optimizer for COACH.
%}

The problem of compiling sequential programs for parallel computers
has been studied since the advent of the first parallel architectures 
in the 1970s. The basic approach consists in applying program transformations
which exhibit or increase the potential parallelism, while guaranteeing
the preservation of the program semantics. Most of these transformations
just reorder the operations of the program; some of them modify its
data structures. Dpendences (exact or conservative) are checked to guarantee
the legality of the transformation.

This has lead to the invention of many loop transformations (loop fusion,
loop splitting, loop skewing, loop interchange, loop unrolling, ...)
which interact in a complicated way. More recently, it has been noticed
that all of these are just changes of basis in the iteration domain of
the program. This has lead to the invention of the polyhedral model, in
which the combination of two transformation is simply a matrix product.

As a side effect, it has been observed that the polytope model is a useful
tool for many other optimization, like memory reduction and locality
improvement. Another point is
that the polyhedral domain \emph{stricto sensu} applies only to
very regular programs. Its extension to more general programs is
an active research subject.

%\subsubsection{High Performance Computing}
%Accelerating high-performance computing (HPC) applications with field-programmable
%gate arrays (FPGAs) can potentially improve performance. 
%However, using FPGAs presents significant challenges~\cite{hpc06a}.
%First, the operating frequency of an FPGA is low compared to a high-end microprocessor.
%Second, based on Amdahl law,  HPC/FPGA application performance is unusually sensitive 
%to the implementation quality~\cite{hpc06b}.
%Finally, High-performance computing programmers are a highly sophisticated but scarce 
%resource. Such programmers are expected to readily use new technology but lack the time 
%to learn a completely new skill such as logic design~\cite{hpc07a} . 
%\\
%HPC/FPGA hardware is only now emerging and in early commercial stages, 
%but these techniques have not yet caught up. 
%Thus, much effort is required to develop design tools that translate high level
%language programs to FPGA configurations.