Efficient PIM (Processor-In-Memory) architectures for data-intensive applications

The Data-intensive applications such as media and graphics applications have gained so much importance that it changed the way processors are now designed. Indeed, the special characteristics of data-intensive applications are not easily matched to the capabilities of general-purpose processors. They also impose a prodigious amount of data transfers and computations. In these data-intensive applications, there are kernel sections which dominate the overall execution times.; The processor-memory performance gap has been increasing and it is now the primary obstacle to any performance improvement in computer systems. This performance gap problem is particularly critical for data-intensive applications that require a large amount of data transfers between the processor and memory.; Therefore, this dissertation presents a hardware/software co-design computing paradigm that uses an efficient PIM (Processor-In-Memory) architecture to efficiently execute the kernels of data-intensive applications. The computing paradigm used in this dissertation not only reduces the memory latencies and increases the memory bandwidth but executes the operations inside of the memory where the data are located, thereby reducing the amount of memory interactions. It also executes the operations in parallel by dividing the memory into small segments and by having each of these segments execute the operations concurrently.; This dissertation also demonstrates that there are data sharing and address generation overheads involved when executing operations in parallel using the computing paradigm. Therefore, several architectural techniques are introduced to overcome such overheads. With the computing paradigm used in this dissertation, the memory access- and computation-intensive kernel sections of data-intensive applications are more efficiently executed. A reduction of up to 2034 X in the number of memory accesses and a performance improvement of up to 439 X for execution of the kernel sections of these data-intensive applications has been obtained in our simulations.