Abstract: Systems, devices and methods for data compression using history search for dictionary based compression. Systems, devices and methods may use parallel processing techniques for data compression and encoding. Systems, devices and methods may provide memory search techniques for hardware.

Abstract: A computing system includes at least one processor and at least one module operable by the at least one processor to calculate a tail of a first dataset by determining elements of the first dataset that fall outside of a specified percentile, and determine locations of the first dataset at which elements of the first dataset that fall outside of the specified percentile are located. The at least one module may be operable to calculate a tail of a second dataset by populating a data structure with elements of the second dataset that correspond to the locations of the first dataset, and determining, using the data structure, elements of the second dataset that fall outside of the specified percentile. The at least one module may be operable to output an indication of at least one of the tail of the first dataset or the tail of the second dataset.

Abstract: A computing system includes at least one processor and at least one module operable by the at least one processor to calculate a tail of a first dataset by determining elements of the first dataset that fall outside of a specified percentile, and determine locations of the first dataset at which elements of the first dataset that fall outside of the specified percentile are located. The at least one module may be operable to calculate a tail of a second dataset by populating a data structure with elements of the second dataset that correspond to the locations of the first dataset, and determining, using the data structure, elements of the second dataset that fall outside of the specified percentile. The at least one module may be operable to output an indication of at least one of the tail of the first dataset or the tail of the second dataset.

Abstract: Systems, devices and methods for data compression using history search for dictionary based compression. Systems, devices and methods may use parallel processing techniques for data compression and encoding. Systems, devices and methods may provide memory search techniques for hardware.

Abstract: Systems, devices and methods for data compression using history search for dictionary based compression. Systems, devices and methods may use parallel processing techniques for data compression and encoding. Systems, devices and methods may provide memory search techniques for hardware.

Abstract: Systems, devices and methods for data compression using history search for dictionary based compression. Systems, devices and methods may use parallel processing techniques for data compression and encoding. Systems, devices and methods may provide memory search techniques for hardware.

Abstract: Systems, devices and methods for data compression using history search for dictionary based compression. Systems, devices and methods may use parallel processing techniques for data compression and encoding. Systems, devices and methods may provide memory search techniques for hardware.

Abstract: Systems, devices and methods for data compression using history search for dictionary based compression. Systems, devices and methods may use parallel processing techniques for data compression and encoding. Systems, devices and methods may provide memory search techniques for hardware.

Abstract: Techniques are disclosed for computing a real-time credit risk score. In one example, the method comprises at least one processor generating a computation graph comprising static computation nodes, dynamic computation nodes, and computation edges. The computation graph is a tree. Before receiving the real-time trade, the processor determines a pipeline kernel in the computation graph and computes the respective static information in the pipeline kernel. After computing the static information, the processor receives the real-time trade. The real-time trade is associated with a current exchange of assets for which a real-time credit risk score may be determined and comprises real-time information for use in computing the real-time credit risk score. The processor computes, based on the real-time trade and the computed static information, the dynamic information in the pipeline kernel and computes, based on the computed dynamic information, the real-time credit risk score.

Abstract: Techniques are disclosed for computing a real-time credit risk score. In one example, the method comprises at least one processor generating a computation graph comprising static computation nodes, dynamic computation nodes, and computation edges. The computation graph is a tree. Before receiving the real-time trade, the processor determines a pipeline kernel in the computation graph and computes the respective static information in the pipeline kernel. After computing the static information, the processor receives the real-time trade. The real-time trade is associated with a current exchange of assets for which a real-time credit risk score may be determined and comprises real-time information for use in computing the real-time credit risk score. The processor computes, based on the real-time trade and the computed static information, the dynamic information in the pipeline kernel and computes, based on the computed dynamic information, the real-time credit risk score.

Abstract: Allocating distributed data structures and managing allocation of a symmetric heap can include defining, using a processor, the symmetric heap. The symmetric heap includes a symmetric partition for each process of a partitioned global address space (PGAS) system. Each symmetric partition of the symmetric heap begins at a same starting virtual memory address and has a same global symmetric break. One process of a plurality of processes of the PGAS system is configured as an allocator process that controls allocation of blocks of memory for each symmetric partition of the symmetric heap. Using the processor executing the allocator process, isomorphic fragmentation among the symmetric partitions of the symmetric heap is maintained.

Abstract: Allocating distributed data structures and managing allocation of a symmetric heap can include defining, using a processor, the symmetric heap. The symmetric heap includes a symmetric partition for each process of a partitioned global address space (PGAS) system. Each symmetric partition of the symmetric heap begins at a same starting virtual memory address and has a same global symmetric break. One process of a plurality of processes of the PGAS system is configured as an allocator process that controls allocation of blocks of memory for each symmetric partition of the symmetric heap. Using the processor executing the allocator process, isomorphic fragmentation among the symmetric partitions of the symmetric heap is maintained.