Intel Corporation

Jesmin Jahan Tithi

Justin E Gottschlich

Suresh Srinivasan

Kermin E ChoFleming, Jr

Shengtian Zhou

Joshua Cranmer

Mahesh A Iyer

Niranjan Hasabnis

Celine Lee

Anand Venkat

Fangke Ye

Roshni Iyer

Methods, apparatus, systems, and articles of manufacture to determine execution cost are disclosed. An example apparatus includes memory; instructions included in the apparatus; and processor circuitry to execute the instruction to: cause a plurality of instructions corresponding to a mnemonic to be executed; determine an average execution cost of the plurality of instructions; determine a standard deviation of execution costs of the plurality of instructions; and generate a mapping table including an entry, the entry including the mnemonic in association with the average and the standard deviation.

Methods and apparatus to determine execution cost

Methods, systems, and apparatus for automatic detection of software bugs are disclosed. An example apparatus includes a comparator to compare reference code to input code to detect a source code error in the input code; a graph generator to generate a graphical representation of the reference code or the input code, the graphical representation to identify non-overlapping code regions; and a root cause determiner to determine a root cause of the source code error in the input code, the root cause based on the non-overlapping code regions.

Methods and apparatus for automatic detection of software bugs

Methods, apparatus, systems, and articles of manufacture are disclosed to determine refined context for bug detection. At least one non-transitory machine-readable medium includes instructions that, when executed, cause at least one processor to at least classify a node on a graph, the graph to represent a computer program, the node to contain partial bug context corresponding to the computer program; identify a location of a software bug in the computer program, the location based on the node; determine a static bug context of the software bug using the location of the software bug; determine a dynamic bug context of the software bug using the location of the software bug; and determine a refined bug context based on a merge of the static bug context and the dynamic bug context.

Methods and apparatus to determine refined context for software bug detection and correction

Disclosed examples to detect and annotate backedges in data-flow graphs include: a characteristic detector to store a node characteristic identifier in memory in association with a first node of a dataflow graph; a characteristic comparator to compare the node characteristic identifier with a reference criterion; and a backedge identifier generator to generate a backedge identifier indicative of a backedge between the first node and a second node of the dataflow graph based on the comparison, the memory to store the backedge identifier in association with a connection arc between the first and second nodes.

Methods and apparatus to detect and annotate backedges in a dataflow graph

Disclosed examples to insert buffers in dataflow graphs include: a backedge filter to remove a backedge between a first node and a second node of a dataflow graph, the first node representing a first operation of the dataflow graph, the second node representing a second operation of the dataflow graph; a latency calculator to determine a critical path latency of a critical path of the dataflow graph that includes the first node and the second node, the critical path having a longer latency to completion relative to a second path that terminates at the second node; a latency comparator to compare the critical path latency to a latency sum of a buffer latency and a second path latency, the second path latency corresponding to the second path; and a buffer allocator to insert one or more buffers in the second path based on the comparison performed by the latency comparator.

Growing community of inventors

San Jose, CA, United States of America

Jesmin Jahan Tithi