The most immediate challenge in the realm of parallel processing does not lie as much in the type of hardware architecture used, but in how easy it will be to program the system in question in a real-world environment with acceptable performance. Machines like Imagine use a straightforward single-threaded model with automated dependencies, memory allocation and DMA scheduling. This in itself is a result of the research at MIT and Stanford in finding an optimal "layering of tasks" between programmer, tools and hardware. Programmers beat tools in mapping algorithms to parallel hardware, and tools beat programmers in figuring out smartest memory allocation schemes, etc. Of particular concern are MIMD designs such as Cell, for which the programmer needs to deal with application partitioning across multiple cores and deal with process synchronization and load balancing. Efficient multi-core programming tools are severely lacking today.