to balance the work evenly across multiple cores. Programming truly multithreaded code often requires complex co-ordination of threads and can easily introduce subtle and difficult-to-find bugs due to the interweaving of processing on data shared between threads (see thread-safety). Consequently, such code is much more difficult to debug than single-threaded code when it breaks. There has been a perceived lack of motivation for writing consumer-level threaded applications because of the relative rarity of consumer-level demand for maximum use of computer hardware. Also, serial tasks like decoding the entropy encoding algorithms used in video codecs are impossible to parallelize because each result generated is used to help create the next result of the entropy decoding algorithm. Given the increasing emphasis on multi-core chip design, stemming from the grave thermal and power consumption problems posed by any further significant increase in processor clock speeds, the extent to which software can be multithreaded to take advantage of these new chips is likely to be the single greatest constraint on computer performance in the future. If developers are unable to design software to fully exploit the resources provided by multiple cores, then they will ultimately reach an insurmountable performance ceiling. The telecommunications market had been one of the first that needed a new design of parallel datapath packet processing because there was a very quick adoption of these multiple-core processors for the datapath and the control plane. These MPUs are going to replace the traditional Network Processors that were based on proprietary microcode or picocode. Parallel programming techniques can benefit from multiple cores directly. Some existing parallel programming models such as Cilk Plus, OpenMP, OpenHMPP, FastFlow, Skandium, MPI, and Erlang can be used on multi-core platforms. Intel introduced a new abstraction for C++ parallelism called TBB. Other research efforts include the Codeplay Sieve System, Cray's Chapel, Sun's Fortress, and IBM's X10. Multi-core processing has also affected the ability of modern computational software development. Developers programming in newer languages might find that their modern languages do not support multi-core functionality. This then requires the use of numerical libraries to access code written in languages like C and Fortran, which perform math computations faster than newer languages like C#. Intel's MKL and AMD's ACML are written in these native languages and take advantage of multi-core processing. Balancing the application workload across processors can be problematic, especially if they have different performance characteristics. There are different conceptual