Application Domains
New Software and Platforms
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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## Section: New Results

### Compilation and Synthesis for Reconfigurable Platform

#### Compile Time Simplification of Sparse Matrix Code Dependences

Participant : Tomofumi Yuki.

Analyzing array-based computations to determine data dependences is useful for many applications including automatic parallelization, race detection, computation and communication overlap, verification, and shape analysis. For sparse matrix codes, array data dependence analysis is made more difficult by the use of index arrays that make it possible to store only the nonzero entries of the matrix (e.g., in $A\left[B\left[i\right]\right]$, $B$ is an index array). Here, dependence analysis is often stymied by such indirect array accesses due to the values of the index array not being available at compile time. Consequently, many dependences cannot be proven unsatisfiable or determined until runtime. Nonetheless, index arrays in sparse matrix codes often have properties such as monotonicity of index array elements that can be exploited to reduce the amount of runtime analysis needed. In this work, we contribute a formulation of array data dependence analysis that includes encoding index array properties as universally quantified constraints. This makes it possible to leverage existing SMT solvers to determine whether such dependences are unsatisfiable and significantly reduces the number of dependences that require runtime analysis in a set of eight sparse matrix kernels. Another contribution is an algorithm for simplifying the remaining satisfiable data dependences by discovering equalities and/or subset relationships. These simplifications are essential to make a runtime-inspection-based approach feasible.

#### Automatic Parallelization Techniques for Time-Critical Systems

Participants : Steven Derrien, Mickael Dardaillon.

Real-time systems are ubiquitous, and many of them play an important role in our daily life. In hard real-time systems, computing the correct results is not the only requirement. In addition, the results must be produced within pre-determined timing constraints, typically deadlines. To obtain strong guarantees on the system temporal behavior, designers must compute upper bounds of the Worst-Case Execution Times (WCET) of the tasks composing the system. WCET analysis is confronted with two challenges: (i) extracting knowledge of the execution flow of an application from its machine code, and (ii) modeling the temporal behavior of the target platform. Multi-core platforms make the latter issue even more challenging, as interference caused by concurrent accesses to shared resources have also to be modeled. Accurate WCET analysis is facilitated by predictable hardware architectures. For example, platforms using ScratchPad Memories (SPMs) instead of caches are considered as more predictable. However SPM management is left to the programmer-managed, making them very difficult to use, especially when combined with complex loop transformations needed to enable task level parallelization. Many researches have studied how to combine automatic SPM management with loop parallelization at the compiler level. It has been shown that impressive average-case performance improvements could be obtained on compute intensive kernels, but their ability to reduce WCET estimates remains to be demonstrated, as the transformed code does not lend itself well to WCET analysis.

In the context of the ARGO project, and in collaboration with members of the PACAP team, we have studied how parallelizing compilers techniques should be revisited in order to help WCET analysis tools. More precisely, we have demonstrated the ability of polyhedral optimization techniques to reduce WCET estimates in the case of sequential codes, with a focus on locality improvement and array contraction. We have shown on representative real-time image processing use cases that they could bring significant improvements of WCET estimates (up to 40%) provided that the WCET analysis process is guided with automatically generated flow annotations [34]. Our current research direction aims [41] at studying the impact of compiler optimization on WCET estimates, and develop specific WCET aware compiler optimization flows. More specifically, we explore the use of iterative compilation (WCET-directed program optimization to explore the optimization space), with the objective to (i) allow flow facts to be automatically found and (ii) select optimizations that result in the lowest WCET estimates. We also explore to which extent code outlining helps, by allowing the selection of different optimization options for different code snippets of the application.

#### Design of High Throughput Mathematical Function Evaluators

Participant : Silviu Ioan Filip.

The evaluation of mathematical functions is a core component in many computing applications and has been a core topic in computer arithmetic since the inception of the field. In [28], we proposed an automatic method for the evaluation of functions via polynomial or rational approximations and its hardware implementation, on FPGAs. These approximations are evaluated using Ercegovac's iterative E-method adapted for FPGA implementation. The polynomial and rational function coefficients are optimized such that they satisfy the constraints of the E-method. It allows for an effective way to perform design space exploration when targeting high throughput.

#### Robust Tools for Computing Rational Chebyshev Approximations

Participant : Silviu Ioan Filip.

Rational functions are useful in a plethora of applications, including digital signal processing and model order reduction. They are nevertheless known to be much harder to work with in a numerical context than other, potentially less expressive families of approximating functions, like polynomials. In [19] we have proposed the use of a numerically robust way of representing rational functions, the barycentric form (i.e., a ratio of partial fractions sharing the same poles). We use this form to develop scalable iterative algorithms for computing rational approximations to functions which minimize the uniform norm error. Our results are shown to significantly outperform previous state of the art approaches.