Intel Corporation

Adarsh Chauhan

Sreenivas Subramoney

Jayesh Gaur

Zeev Sperber

Lihu Rappoport

Adi Yoaz

Franck Sala

Vinodh Gopal

Vedvyas Shanbhogue

Wajdi K Feghali

Hong Wang

Stanislav Shwartsman

Kamil Garifullin

Sumeet Bandishte

Methods and apparatus relating to an instruction and/or micro-architecture support for decompression on core are described. In an embodiment, decode circuitry decodes a decompression instruction into a first micro operation and a second micro operation. The first micro operation causes one or more load operations to fetch data into one or more cachelines of a cache of a processor core. Decompression Engine (DE) circuitry decompresses the fetched data from the one or more cachelines of the cache of the processor core in response to the second micro operation. Other embodiments are also disclosed and claimed.

Instruction and micro-architecture support for decompression on core

Methods and apparatus relating to an Application Programming Interface (API) for fine grained low latency decompression within a processor core are described. In an embodiment, a decompression Application Programming Interface (API) receives an input handle to a data object. The data object includes compressed data and metadata. Decompression Engine (DE) circuitry decompresses the compressed data to generate uncompressed data. The DE circuitry decompress the compressed data in response to invocation of a decompression instruction by the decompression API. The metadata comprises a first operand to indicate a location of the compressed data, a second operand to indicate a size of the compressed data, a third operand to indicate a location to which decompressed data by the DE circuitry is to be stored, and a fourth operand to indicate a size of the decompressed data. Other embodiments are also disclosed and claimed.

Application programming interface for fine grained low latency decompression within processor core

A processor comprises a microarchitectural feature and dynamic tuning unit (DTU) circuitry. The processor executes a program for first and second execution windows with the microarchitectural feature disabled and enabled, respectively. The DTU circuitry automatically determines whether the processor achieved worse performance in the second execution window. In response to determining that the processor achieved worse performance in the second execution window, the DTU circuitry updates a usefulness state for a selected address of the program to denote worse performance. In response to multiple consecutive determinations that the processor achieved worse performance with the microarchitectural feature enabled, the DTU circuitry automatically updates the usefulness state to denote a confirmed bad state. In response to the usefulness state denoting the confirmed bad state, the DTU circuitry automatically disables the microarchitectural feature for the selected address for execution windows after the second execution window. Other embodiments are described and claimed.

Technology for dynamically tuning processor features

Systems, methods, and apparatuses relating to hardware for auto-predication of critical branches. In one embodiment, a processor core includes a decoder to decode instructions into decoded instructions, an execution unit to execute the decoded instructions, a branch predictor circuit to predict a future outcome of a branch instruction, and a branch predication manager circuit to disable use of the predicted future outcome for a conditional critical branch comprising the branch instruction.

Detecting a dynamic control flow re-convergence point for conditional branches in hardware

A processing device includes a branch IP table and branch predication circuitry coupled to the branch IP table. The branch predication circuitry to: determine a dynamic convergence point in a conditional branch of set of instructions; store the dynamic convergence point in the branch IP table; fetch a first and second speculative path of the conditional branch; while determining which of the first speculative path and the second speculative path is a taken path of the conditional branch and determining whether a dynamic convergence point is fetched corresponding to the stored dynamic convergence point, stall scheduling of instructions of the first speculative path and the second speculative path; and in response to determining that one of the first speculative path and the second speculative path is the taken path and the fetched dynamic convergence point corresponds to the stored convergence point, resume scheduling of the instructions of the taken path.

Automatic predication of hard-to-predict convergent branches

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Bengaluru, India

Adarsh Chauhan