Using Machine Learning to yield Scalable Program Analyses Program Analysis tackles the problem of predicting the behavior or certain properties of the considered program code. The challenge lies in determining the dynamic runtime behavior statically at compile time. While in rare cases it is possible to determine exact dynamic properties already statically, in many cases, e.g., in analyzing memory dependencies, one can only find imprecise information. To overcome this, we apply Machine Learning (ML) techniques which are particularly suited for this task. They yield highly scalable predictors and are safely applicable when erroneous predictions merely have an impact on program optimality but not on correctness. In this talk, I pr...
Scalability is a fundamental problem in computer science. Computer scientists often describe the sc...
Machine learning techniques are applicable to computer system optimization. We show that shared memo...
We present a new technique for identifying scalability bottle-necks in executions of single-program,...
As the existing techniques that empower the modern high-performance processors are being refined and...
Modern day hardware platforms are parallel and diverse, ranging from mobiles to data centers. Mains...
AbstractAbstract interpretation is a technique for the static detection of dynamic properties of pro...
Abstract interpretation is a technique for the static detection of dynamic properties of programs. I...
Improving the reliability and performance are of utmost importance for any system. This thesis prese...
Computer memory hierarchy becomes increasingly powerful but also more complex to optimize. Run-time...
Parallelism is everywhere, with co-processors such as Graphics Processing Units (GPUs) accelerating ...
The ever-increasing computational power of contemporary microprocessors reduces the execution time s...
International audienceMemory Dependency Prediction (MDP) is paramount to good out-of-order performan...
The ever-increasing computational power of contemporary microprocessors reduces the execution time s...
Modeling the evolution of the state of program memory during program execution is critical to many p...
Leveraging the power of scratchpad memories (SPMs) available in most embedded systems today is cruci...
Scalability is a fundamental problem in computer science. Computer scientists often describe the sc...
Machine learning techniques are applicable to computer system optimization. We show that shared memo...
We present a new technique for identifying scalability bottle-necks in executions of single-program,...
As the existing techniques that empower the modern high-performance processors are being refined and...
Modern day hardware platforms are parallel and diverse, ranging from mobiles to data centers. Mains...
AbstractAbstract interpretation is a technique for the static detection of dynamic properties of pro...
Abstract interpretation is a technique for the static detection of dynamic properties of programs. I...
Improving the reliability and performance are of utmost importance for any system. This thesis prese...
Computer memory hierarchy becomes increasingly powerful but also more complex to optimize. Run-time...
Parallelism is everywhere, with co-processors such as Graphics Processing Units (GPUs) accelerating ...
The ever-increasing computational power of contemporary microprocessors reduces the execution time s...
International audienceMemory Dependency Prediction (MDP) is paramount to good out-of-order performan...
The ever-increasing computational power of contemporary microprocessors reduces the execution time s...
Modeling the evolution of the state of program memory during program execution is critical to many p...
Leveraging the power of scratchpad memories (SPMs) available in most embedded systems today is cruci...
Scalability is a fundamental problem in computer science. Computer scientists often describe the sc...
Machine learning techniques are applicable to computer system optimization. We show that shared memo...
We present a new technique for identifying scalability bottle-necks in executions of single-program,...