Baseband processing is an important and computationally heavy part of modern mobile cellular systems. These systems use specialized hardware that has many digital signal processing cores and hardware accelerators. The algorithms that run on these systems are complexand needs to take advantage of this hardware. Developing software for these systems requires domain knowledge about baseband processing and low level programming on parallel real time systems. This thesis investigates if the programming language Julia can be used to implement algorithms for baseband processing in mobile telephony base stations. If it is possible to use a scientific language like Julia to directly implement programs for the special hardware in the base stations it can reduce lead times and costs. In this thesis a uplink receiver is implemented in Julia. This implementation is written usinga domain specific language. This makes it possible to specify a number of transformations that use the metaprogramming capabilities in Julia to transform the uplink receiver such that it is better suited to execute on the hardware described above. This is achieved by transforming the program such that it consists of functions that either can be executed on single digital signal processing cores or hardware accelerators. It is concluded that Julia seems suited for prototyping baseband processing algorithms. Using metaprogramming to transform a baseband processing algorithm to be better suited for baseband processing hardware is also a feasible approach.

Loop vectorization is a powerful optimization technique that can significantly boost the runtime of loops. This optimization depends on functional equivalence between the original and optimized code versions, a requirement typically established through the compiler's static analysis. When this condition is not met, the compiler will miss the optimization. The process of manually rewriting the source code to pass an already missed compiler optimization is time-consuming, given the multitude of potential code variations, and demands a high level of expertise, making it impractical in many scenarios. In this work, we propose a novel framework that aims to take the code blocks that the compiler failed to optimize and transform them to another code block that passes the compiler optimization. We develop an algorithm to efficiently search for a code structure that automatically passes the compiler optimization (weakly verified through a correctness test). We focus on loop-vectorize optimization inside OpenMP directives, where the introduction of parallelism adds complexity to the compiler's vectorization task and is shown to hinder optimizations. Furthermore, we introduce a modified version of TSVC, a loop vectorization benchmark in which all original loops are executed within OpenMP directives. Our evaluation shows that our framework enables " loop-vectorize" optimizations that the compiler failed to pass, resulting in a speedup up to 340× in the blocks optimized. Furthermore, applying our tool to HPC benchmark applications, where those applications are already built with optimization and performance in mind, demonstrates that our technique successfully enables extended compiler optimization, thereby accelerating the execution time of the optimized blocks in 15 loops and the entire execution time of the three applications by up to 1.58 times. / Master of Science / Loop vectorization is a powerful technique for improving the performance of specific sections in computer programs known as loops. Particularly, it simultaneously executes instructions of different iterations in a loop, providing a considerable speedup on its runtime due to this parallelism. To apply this optimization, the code needs to meet certain conditions, which are usually checked by the compiler. However, sometimes the compiler cannot verify these conditions, and the optimization fails. Our research introduces a new approach to fix these issues automatically. Normally, fixing the code manually to meet these conditions is time-consuming and requires high expertise. To overcome this, we've developed a tool that can efficiently find ways to make the code satisfy the conditions needed for optimization. Our focus is on a specific type of code that uses OpenMP directives to split the loop on multiple processor cores and runs them simultaneously, where adding this parallelism makes the code more complex for the compiler to optimize. Our tests show that our approach successfully improves the speed of computer programs by enabling optimizations initially missed by the compiler. This results in significant speed improvements for specific parts of the code, sometimes up to 340 times faster. We've also applied our method to well-optimized computer programs, and it still managed to make them run up to 1.58 times faster.

<p>The introduction of Java 5.0 came along with an extension of the language syntax. Several new language features as generic types and enumeration types were added to the language specification. These features cause downward-incompatibilities, code written in Java 5.0 will not work on older versions of the Java runtime environment. For some active projects, however, it is not possible to upgrade to higher Java versions, since some code might not be supported on Java 5.0. If one still wants to use components written in Java 5.0, these must be downgraded. Up to now this has been accomplished mostly by transforming the byte code of these programs.</p><p>In this thesis, we present a set of transformations which transform Java 5.0 source code to Java 1.4 compatible code. We successfully apply these transformations to two larger projects and compare our approach to the up to now common byte-code based tools.</p>

The introduction of Java 5.0 came along with an extension of the language syntax. Several new language features as generic types and enumeration types were added to the language specification. These features cause downward-incompatibilities, code written in Java 5.0 will not work on older versions of the Java runtime environment. For some active projects, however, it is not possible to upgrade to higher Java versions, since some code might not be supported on Java 5.0. If one still wants to use components written in Java 5.0, these must be downgraded. Up to now this has been accomplished mostly by transforming the byte code of these programs. In this thesis, we present a set of transformations which transform Java 5.0 source code to Java 1.4 compatible code. We successfully apply these transformations to two larger projects and compare our approach to the up to now common byte-code based tools.

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<p>In this research paper, it evaluates the benefits achievable from static testing framework by analyzing and transforming the <em>JUnit3.8 </em>source code and static execution of transformed code. Static structure enables us to analyze the code statically during creation and execution of test cases. The concept of research is by now well established in static analysis and testing development. The research approach is also increasingly affecting the static testing process and such research oriented work has proved particularly valuable for those of us who want to understand the reflective behavior of <em>JUnit3.8 Framework</em>.</p><p><em> JUnit3.8 Framework</em> uses <em>Java Reflection API</em> to invoke core functionality (test cases creation and execution) dynamically. However, <em>Java Reflection API</em> allows developers to access and modify structure and behavior of a program.  Reflection provides flexible solution for creating test cases and controlling the execution of test cases. Java reflection helps to encapsulate test cases in a single object representing the test suite. It also helps to associate each test method with a test object. Where reflection is a powerful tool to perform potential operations, on the other hand, it limits static analysis. Static analysis tools often cannot work effectively with reflection.</p><p>In order to avoid the reflection, <em>Static Testing Framework</em> provides a static platform to analyze the <em>JUnit3.8</em> source code and transform it into non-reflective version that emulates the dynamic behavior of <em>JUnit3.8</em>. The transformed source code has possible leverage to replace reflection with static code and does same things in an execution environment of <em>Static Testing Framework</em> that reflection does in <em>JUnit3.8</em>. More besides, the transformed code also enables execution environment of <em>Static Testing Framework</em> to run test methods statically. In order to measure the degree of efficiency, the implemented tool is evaluated. The evaluation of <em>Static Testing Framework</em> draws results for different Java projects and these statistical data is compared with <em>JUnit3.8</em> results to measure the effectiveness of <em>Static Testing Framework</em>. As a result of evaluation, <em>STF</em> can be used for static creation and execution of test cases up to <em>JUnit3.8</em> where test cases are not creating within a test class and where real definition of constructors is not required. These problems can be dealt as future work by introducing a middle layer to execute test fixtures for each test method and by generating test classes as per real definition of constructors.</p>

In this research paper, it evaluates the benefits achievable from static testing framework by analyzing and transforming the JUnit3.8 source code and static execution of transformed code. Static structure enables us to analyze the code statically during creation and execution of test cases. The concept of research is by now well established in static analysis and testing development. The research approach is also increasingly affecting the static testing process and such research oriented work has proved particularly valuable for those of us who want to understand the reflective behavior of JUnit3.8 Framework. JUnit3.8 Framework uses Java Reflection API to invoke core functionality (test cases creation and execution) dynamically. However, Java Reflection API allows developers to access and modify structure and behavior of a program.  Reflection provides flexible solution for creating test cases and controlling the execution of test cases. Java reflection helps to encapsulate test cases in a single object representing the test suite. It also helps to associate each test method with a test object. Where reflection is a powerful tool to perform potential operations, on the other hand, it limits static analysis. Static analysis tools often cannot work effectively with reflection. In order to avoid the reflection, Static Testing Framework provides a static platform to analyze the JUnit3.8 source code and transform it into non-reflective version that emulates the dynamic behavior of JUnit3.8. The transformed source code has possible leverage to replace reflection with static code and does same things in an execution environment of Static Testing Framework that reflection does in JUnit3.8. More besides, the transformed code also enables execution environment of Static Testing Framework to run test methods statically. In order to measure the degree of efficiency, the implemented tool is evaluated. The evaluation of Static Testing Framework draws results for different Java projects and these statistical data is compared with JUnit3.8 results to measure the effectiveness of Static Testing Framework. As a result of evaluation, STF can be used for static creation and execution of test cases up to JUnit3.8 where test cases are not creating within a test class and where real definition of constructors is not required. These problems can be dealt as future work by introducing a middle layer to execute test fixtures for each test method and by generating test classes as per real definition of constructors.