Abstract: A method and apparatus for efficiently processing data in various formats in a single instruction multiple data (“SIMD”) architecture is presented. Specifically, a method to unpack a fixed-width bit values in a bit stream to a fixed width byte stream in a SIMD architecture is presented. A method to unpack variable-length byte packed values in a byte stream in a SIMD architecture is presented. A method to decompress a run length encoded compressed bit-vector in a SIMD architecture is presented. A method to return the offset of each bit set to one in a bit-vector in a SIMD architecture is presented. A method to fetch bits from a bit-vector at specified offsets relative to a base in a SIMD architecture is presented. A method to compare values stored in two SIMD registers is presented.

Abstract: Techniques are provided for more efficiently using the bandwidth of the I/O path between a CPU and volatile memory during the performance of database operation. Relational data from a relational table is stored in volatile memory as column vectors, where each column vector contains values for a particular column of the table. A binary-comparable format may be used to represent each value within a column vector, regardless of the data type associated with the column. The column vectors may be compressed and/or encoded while in volatile memory, and decompressed/decoded on-the-fly within the CPU. Alternatively, the CPU may be designed to perform operations directly on the compressed and/or encoded column vector data. In addition, techniques are described that enable the CPU to perform vector processing operations on the column vector values.

Abstract: Techniques are provided for more efficiently using the bandwidth of the I/O path between a CPU and volatile memory during the performance of database operation. Relational data from a relational table is stored in volatile memory as column vectors, where each column vector contains values for a particular column of the table. A binary-comparable format may be used to represent each value within a column vector, regardless of the data type associated with the column. The column vectors may be compressed and/or encoded while in volatile memory, and decompressed/decoded on-the-fly within the CPU. Alternatively, the CPU may be designed to perform operations directly on the compressed and/or encoded column vector data. In addition, techniques are described that enable the CPU to perform vector processing operations on the column vector values.

Abstract: Techniques are provided for more efficiently using the bandwidth of the I/O path between a CPU and volatile memory during the performance of database operation. Relational data from a relational table is stored in volatile memory as column vectors, where each column vector contains values for a particular column of the table. A binary-comparable format may be used to represent each value within a column vector, regardless of the data type associated with the column. The column vectors may be compressed and/or encoded while in volatile memory, and decompressed/decoded on-the-fly within the CPU. Alternatively, the CPU may be designed to perform operations directly on the compressed and/or encoded column vector data. In addition, techniques are described that enable the CPU to perform vector processing operations on the column vector values.

Abstract: A method provided for optimizing a query expression on a database engine of a database server. The query expression is sent to the database engine. The query expression contains a plurality of query language elements. The database engine initiates query processing of the query expression. An evaluation counter within the database engine increments an evaluation counter value corresponding to a query language element result. A flag is set within the database engine when the evaluation counter value reaches a threshold value. The database engine can then evaluate the efficiency of the query language elements in the query expression by comparing the evaluation counter value for each query language element against a set of optimization criteria.

Abstract: A method provided for optimizing a query expression on a database engine of a database server. The query expression is sent to the database engine. The query expression contains a plurality of query language elements. The database engine initiates query processing of the query expression. An evaluation counter within the database engine increments an evaluation counter value corresponding to a query language element result. A flag is set within the database engine when the evaluation counter value reaches a threshold value. The database engine can then evaluate the efficiency of the query language elements in the query expression by comparing the evaluation counter value for each query language element against a set of optimization criteria.

Abstract: A method and apparatus to insert special instruction. At least one of the illustrative embodiments is a method comprising converting a first representation of a computer program to a second representation, and inserting into the second representation a special instruction not needed to implement functionality in the first representation. The special instruction gives duplicate copies of the computer program executed in different processors an opportunity to service external asynchronous interrupts.

Abstract: One embodiment relates to a computer-implemented method of generating an executable program which includes inserting predicated calls to trace routines during compilation of the source code. Each predicated call comprises a function call that is conditional upon a value stored in a predicate register. The object code generated from compiling said source code is subsequently linked with object code which includes the trace routines. Another embodiment relates to a computer-implemented method of executing a deployed computer program with low-level tracing using compiler-inserted predicated tracing calls. A tracing mode is enabled by setting one or more predicate register bits in a microprocessor. Predicated calls to trace routines insert trace data into at least one trace buffer. Upon a system crash, a core file including said trace data is written out. Other embodiments, aspects and features are also disclosed.