Abstract: A just-in-time (JIT) compiler binds constants to specific memory locations at runtime. The JIT compiler parses program code derived from a multithreaded application and identifies an instruction that references a uniform constant. The JIT compiler then determines a chain of pointers that originates within a root table specified in the multithreaded application and terminates at the uniform constant. The JIT compiler generates additional instructions for traversing the chain of pointers and inserts these instructions into the program code. A parallel processor executes this compiled code and, in doing so, causes a thread to traverse the chain of pointers and bind the uniform constant to a uniform register at runtime. Each thread in a group of threads executing on the parallel processor may then access the uniform constant.

Abstract: A compiler parses a multithreaded application into cohesive blocks of instructions. Cohesive blocks include instructions that do not diverge or converge. Each cohesive block is associated with one or more uniform registers. When a set of threads executes the instructions in a given cohesive block, each thread in the set may access the uniform register independently of the other threads in the set. Accordingly, the uniform register may store a single copy of data on behalf of all threads in the set of threads, thereby conserving resources.

Abstract: Systems, methods and computer readable medium for training a risk rating system for assessing a risk of a mobile application are disclosed. One or more features representing operational characteristics of mobile applications and malware are extracted. A first learning classifier and a second learning classifier are trained using the extracted features. A machine learning risk rating model is generated, based on the combination of the first learning classifier and the second learning classifier to calculate a risk rating based on the features and a correlation of the features. Systems, methods, and computer readable medium for assessing a risk for a mobile application are also disclosed. One or more features of a mobile application are extracted. A learning classifier is applied to the extracted features. A risk rating is determined based on the result of the classifier.

Abstract: A compiler parses a multithreaded application into cohesive blocks of instructions. Cohesive blocks include instructions that do not diverge or converge. Each cohesive block is associated with one or more uniform registers. When a set of threads executes the instructions in a given cohesive block, each thread in the set may access the uniform register independently of the other threads in the set. Accordingly, the uniform register may store a single copy of data on behalf of all threads in the set of threads, thereby conserving resources.

Abstract: A just-in-time (JIT) compiler binds constants to specific memory locations at runtime. The JIT compiler parses program code derived from a multithreaded application and identifies an instruction that references a uniform constant. The JIT compiler then determines a chain of pointers that originates within a root table specified in the multithreaded application and terminates at the uniform constant. The JIT compiler generates additional instructions for traversing the chain of pointers and inserts these instructions into the program code. A parallel processor executes this compiled code and, in doing so, causes a thread to traverse the chain of pointers and bind the uniform constant to a uniform register at runtime. Each thread in a group of threads executing on the parallel processor may then access the uniform constant.

Abstract: Systems, methods and computer readable medium for training a risk rating system for assessing a risk of a mobile application are disclosed. One or more features representing operational characteristics of mobile applications and malware are extracted. A first learning classifier and a second learning classifier are trained using the extracted features. A machine learning risk rating model is generated, based on the combination of the first learning classifier and the second learning classifier to calculate a risk rating based on the features and a correlation of the features. Systems, methods, and computer readable medium for assessing a risk for a mobile application are also disclosed. One or more features of a mobile application are extracted. A learning classifier is applied to the extracted features. A risk rating is determined based on the result of the classifier.