A computer-implemented method (and corresponding system) is provided, that enables or facilitates the execution of a portion of source code, written in a high-level language (HLL), on a blockchain platform. The method and system can include a blockchain compiler, arranged to convert a portion of high-level source code into a form that can be used with a blockchain platform. This may be the Bitcoin blockchain or an alternative. The method can include: receiving the portion of source code as input; and generating an output script comprising a plurality of op_codes. The op_codes are a subset of op_codes that are native to a functionally-restricted, blockchain scripting language. The outputted script is arranged and/or generated such that, when executed, the script provides, at least in part, the functionality specified in the source code. The blockchain scripting language is restricted such that it does not natively support complex control-flow constructs or recursion via jump-based loops or other recursive programming constructs. The step of generating the output script may comprise the unrolling at least one looping construct provided in the source code. The method may further comprise providing or using an interpreter or virtual machine arranged to convert the output script into a form that is executable on a blockchain platform.

Techniques for representing a native function using an executable reference are disclosed. The system receives an instruction to create an executable reference for a native function, including a method type comprising a method signature corresponding to the executable reference, and a function description including (a) a memory layout corresponding to data returned by the function and (b) memory layouts corresponding to parameters required by the function. The system selects an application binary interface (ABI). The system generates code that, for each parameter, of the one or more parameters required by the function, converts the parameter from a value formatted for use by a Java Virtual machine to a value formatted for use in the native function, based on the selected ABI. Responsive to invocation of the executable reference, the generated code and the native function may be executed.

Systems and methods for fetchless acceleration of convolutional loops on an integrated circuit include identifying, by a compiler, finite state machine (FSM) initialization parameters based on computational requirements of a computational loop; initializing a programmable FSM based on the FSM initialization parameters, wherein the FSM initialization parameters include a loop iteration parameter identifying a number of computation cycles of the computational loop; executing the programmable FSM to enable fetchless computations by: generating a plurality of computational loop control signals including a distinct computation loop control signal for each of the number of computation cycles of the computational loop based on the loop iteration parameter; and controlling an execution of a plurality of computation cycles of a computational circuit performing the computational loop based on transmitting the plurality of computational loop control signals until the number of computation cycles of the computation loop are completed.

Apparatuses, systems, and techniques to control operation of a memory cache. In at least one embodiment, cache guidance is specified within application source code by associating guidance with declaration of a memory block, and then applying specified guidance to source code statements that access said memory block.

The technical solutions relate to the technical field of compilation of applications, and particularly to the technical field of mini programs. A developer tool constructs a first compilation result of the hosted APP before compilation optimization and a second compilation result of the hosted APP after the compilation optimization respectively based on a source code of the hosted APP, and uploads them to a management platform. The management platform sends the corresponding compilation results according to environment data of the hosted APP running environment provided by the host APP, so that the host APP uses the hosted APP running environment provided by the host APP to run the obtained compilation result.

Methods, apparatus and computer software product for optimization of data transfer between two memories includes determining access to master data stored in one memory and/or to local data stored in another memory such that either or both of the size of total data transferred and the number of data transfers required to transfer the total data can be minimized. The master and/or local accesses are based on, at least in part, respective structures of the master and local data.

A monitoring system monitors processing of incoming messages by an application, and logs data related to performance of the application. The application includes a plurality of checkpoints, and the monitoring system logs data upon each message traversing the checkpoints in the application. The monitoring system is configured to dynamically modify checkpoints within the application based on latency detection of portions of the application, resulting in improved granularity/resolution of the data collected from congested portions of the application, and reducing the performance penalty of the monitoring system from portions of the application that are not congested.

A computerized technique for program simplification and specialization combines a partial interpretation of the program based on a subset of program functions to obtain variable states with concrete values at a program “neck.” These concrete values are then propagated as part of an optimization transformation that simplifies the program based on these constant values, for example, by eliminating branches that are never taken based on the constant values.

A processing system executes a specialized wavefront, referred to as a “garbage collecting wavefront” or GCWF, to identify and deallocate resources such as, for example, scalar registers, vector registers, and local data share space, that are no longer being used by wavefronts of a workgroup executing at the processing system (i.e., dead resources). In some embodiments, the GCWF is programmed to have compiler information regarding the resource requirements of the other wavefronts of the workgroup and specifies the program counter after which there will be a permanent drop in resource requirements for the other wavefronts. In other embodiments, the standard compute wavefronts signal the GCWF when they have completed using resources. The GCWF sends a command to deallocate the dead resources so the dead resources can be made available for additional wavefronts.

Embodiments for locating performance hot spots include collecting sample data having instruction addresses, the sample data being for a neural network model and determining instructions in the instruction addresses that are performance hot spots. A listing file is used to map the instructions of the sample data that are performance hot spots to locations in a lower-level intermediate representation. A mapping file is used to map the locations of the lower-level intermediate representation that are performance hot spots to operations in one or more higher-level representations, one or more of the operations corresponding to the performance hot spots, the mapping file being generated from compiling the neural network model.