In a sequence of major computational steps or in an iterative computation, a stencil amplifier can increase the number of data elements accessed from one or more data structures in a single major step or iteration, thereby decreasing the total number of computations and/or communication operations in the overall sequence or the iterative computation. Stencil amplification, which can be optimized according to a specified parameter such as compile time, rune time, code size, etc., can improve the performance of a computing system executing the sequence or the iterative computation in terms of run time, memory load, energy consumption, etc. The stencil amplifier typically determines boundaries, to avoid erroneously accessing data elements not present in the one or more data structures.

A method and system may be provided for recording discussions about computer code in an integrated development environment (“IDE”). In some aspects, a communication channel is integrated with an IDE. Communications and discussions may be tracked and linked with specific code sections.

A method and system may be provided for recording discussions about computer code in an integrated development environment (“IDE”). In some aspects, a communication channel is integrated with an IDE. Communications and discussions may be tracked and linked with specific code sections.

Techniques are provided for allocation of shared computing resources using source code feature extraction and machine learning techniques. An exemplary method comprises obtaining source code for execution in a shared computing environment; extracting a plurality of discriminative features from the source code; obtaining a trained machine learning model; and generating a prediction of an allocation of one or more resources of the shared computing environment needed to satisfy one or more service level agreement requirements for the source code. The generated prediction is optionally adjusted using a statistical analysis of an error curve, based on one or more error boundaries obtained by the trained machine learning model. The trained machine learning model can be trained using a set of discriminative features extracted from training source code and corresponding measurements of metrics of the service level agreement requirements obtained by executing the training source code on a plurality of the resources of the shared computing environment.

Systems and methods are provided for implementing a machine learning (ML) model based compiler, language translator, and/or a decompiler. For example, the system may receive a first source code in a first programming language, tokenize the first source code file forming tokenized code, generate a sequence vector of tokenized code, and provide the sequence vector of tokenized code as input to a trained ML model compiler. The output of the trained ML model compiler may create a second executable file or the source code in a second programming language.

Systems and methods are provided for implementing a machine learning (ML) model based compiler, language translator, and/or a decompiler. For example, the system may receive a first source code in a first programming language, tokenize the first source code file forming tokenized code, generate a sequence vector of tokenized code, and provide the sequence vector of tokenized code as input to a trained ML model compiler. The output of the trained ML model compiler may create a second executable file or the source code in a second programming language.

Apparatuses, systems, and techniques to add operators to a compiler. In at least one embodiment, one or more operators are added to a compiler using one or more application programming interfaces (APIs).

Various computing technologies for various reverse engineering platforms capable of outputting, including creating or generating, a human readable and high level source code, such as C, Fortran, LISP, or BASIC, from various binary files, such as application binaries, executable binaries, or data binaries, in an original language as developed pre-compilation. For example, some of such reverse engineering platforms can be programmed to disassemble binary files from different process architectures, identify various code optimizations as compiler introduced, reverse or unwind various compiler optimizations (de-optimize), and generate a human readable and high-level source code from de-optimized data.

Various computing technologies for various reverse engineering platforms capable of outputting, including creating or generating, a human readable and high level source code, such as C, Fortran, LISP, or BASIC, from various binary files, such as application binaries, executable binaries, or data binaries, in an original language as developed pre-compilation. For example, some of such reverse engineering platforms can be programmed to disassemble binary files from different process architectures, identify various code optimizations as compiler introduced, reverse or unwind various compiler optimizations (de-optimize), and generate a human readable and high-level source code from de-optimized data.

Recompiling code based on sustainability. Code is recompiled in a manner that accounts for sustainability values. When a deployment request is received, sustainability values are identified. The resources needed to fulfill the deployment request are identified based on the sustainability values and available resources. Once the resources that are likely to best meet the sustainability values are identified, the code is recompiled accordingly.