Systems, methods, and devices for original code emulation for performance monitoring is provided. A system may memory to store instructions. A processor may implement an instruction converter in hardware or software to convert the instructions to translated code. Specifically, the instruction converter receives the instructions and translates the stored instructions into the translated code that includes one or more indexed instructions. The one or more indexed instructions include a field indicating a number of branches in the stored instructions that are taken in the translated code.

An approach is provided for optimizing a just-in-time (JIT) compilation process. A source pod in a container orchestrated execution environment is determined to be saturated. Profile data from a JIT compiler, a virtual machine state, and a native-compiled code state are collected. The profile data, virtual machine state, and native-compiled code state are stored in a data structure in a persistent data repository. In response to a restart or a redeployment of the source pod and an application running on the source pod, the stored profile data, virtual machine state, and native-compiled code state are reused in a new target pod, without requiring a monitoring and an identification of hot code areas in the application after the source pod becomes saturated.

Computer-implemented methods, non-transitory, computer-readable media, and computer-implemented systems are provided for executing a smart contract in a blockchain network. A computer-implemented method includes: in response to determining that bytecodes of a smart contract are deployed on a first blockchain node in a blockchain network, starting, by the first blockchain node, to compile the bytecodes of the smart contract into machine codes of the smart contract through a Just-In-Time (JIT) compiler; determining, by the first blockchain node, that the machine codes of the smart contract are not locally stored and that execution results of the machine codes of the smart contract and the bytecodes of the smart contract are consistent; and in response to the determining, performing, by the first blockchain node, interpretation execution on the bytecodes of the smart contract.

Disclosed herein are system, method, and computer program product embodiments for determining an appropriate FPGA for a particular computer program. An embodiment operates by a central processing unit's counter identifying a plurality of workload properties in processing a computer program, wherein the central processing unit is part of a first computer architecture. The central processing unit then sends the workload properties to a controller trained to identify a field-programmable gate array (FPGA) module based on the plurality of workload properties. The central processing unit thereafter receives a recommended FPGA module from the controller and implements the recommended FPGA module in a computer architecture for processing the computer program, whereby the second computer architecture is able to perform the computer program more efficiently than the first computer architecture.

This application describes methods, systems, and apparatus, including computer programs encoded on computer storage media, of an AI-assisted compiler. An example method includes obtaining intermediate code and executable code generated by compiling a computer program with a compiler; determining a reward based on one or more traces obtained by executing the executable code in a runtime system; generating an embedding vector based on the intermediate code and the one or more traces to represent code execution states; determining, using a reinforcement learning agent, one or more optimization actions based on the embedding vector and the reward; and updating the compiler by applying the one or more optimization actions.

A rendering optimisation identifies a draw call within a current render (which may be the first draw call in the render or a subsequent draw call in the render) and analyses a last shader in the series of shaders used by the draw call to determine whether the last shader samples from the one or more buffers at coordinates matching a current fragment location. If this determination is positive, the method further recompiles the last shader to replace an instruction that reads data from one of the one or more buffers at coordinates matching a current fragment location with an instruction that reads from the one or more buffers at coordinates stored in on-chip registers.

A system and method for optimizing runtime environments for applications by running the applications in a plurality of runtime environments and iteratively selecting and creating new runtime environments based on a fitness score determined for the plurality of runtime environments.

Computer-implemented methods, non-transitory, computer-readable media, and computer-implemented systems are provided for executing a smart contract in a blockchain network. A computer-implemented method includes: in response to determining that bytecodes of a smart contract are deployed on a first blockchain node in a blockchain network, starting, by the first blockchain node, to compile the bytecodes of the smart contract into machine codes of the smart contract through a Just-In-Time (JIT) compiler; determining, by the first blockchain node, that the machine codes of the smart contract are not locally stored and that execution results of the machine codes of the smart contract and the bytecodes of the smart contract are consistent; and in response to the determining, performing, by the first blockchain node, interpretation execution on the bytecodes of the smart contract.

Techniques usable in optimization processing are described. A system includes an optimization processing unit (OPU). The OPU includes stochastic computing units and at least one programmable interconnect. Each of the stochastic computing units includes nodes and multiplication unit(s) configured to interconnect at least a portion of the nodes. The programmable interconnect(s) are configured to provide weights for and to selectably couple a portion of the stochastic computing units.

Techniques for reducing overhead in native function calls are disclosed. The system may receive a method invocation instruction for invoking a particular method. The method invocation instruction includes a function descriptor, a method type, and an application binary interface (ABI) descriptor. The function descriptor includes a memory layout corresponding to any data returned by the function and memory layouts corresponding to each argument for the particular method. The system can select an ABI for processing the particular method based on the received ABI descriptor. The system can further associate each argument with a corresponding particular physical register into which the argument is to be loaded. The particular register is selected based on at least the selected ABI and the function descriptor. The system can cause a virtual machine to move each argument into the corresponding associated physical register.