Dynamic Random Access Memory (DRAM) is used as the bulk of the main memory in most computing systems and its energy and power consumption has become a first-class design consideration for modern systems. We propose ESKIMO, a scheme where when the program or operating systems memory manager allocates or frees up a memory region, this information is used by the architecture to optimize the working of the DRAM system, particularly to save energy and power. In this work we attempt to have the architecture work hand in hand with information about allocated and freed space provided by the program. We discuss multiple ways to use this information to reduce the energy and power consumption of the memory and present results of this optimization. We evaluate the energy and power benefits of our technique using a publicly available, hardware-validated, DRAM simulator, DRAMsim [1]. Our current studies show very promising results with energy savings on average of 39%.

Estimating the maximum power and thermal characteristics of a processor is essential for designing its power delivery system, packaging, cooling, and power/thermal management schemes. Typical benchmark suites used in performance evaluation do not stress the processor to its limit though, and current practice in industry is to develop artificial benchmarks that are specifically written to generate maximum processor (component) activity. However, manually developing and tuning so called stressmarks is extremely tedious and time-consuming while requiring an intimate understanding of the processor. A synthetic program that can be tuned to produce a variety of benchmark characteristics would significantly help in addressing this problem by enabling the automatic exploration of the large temperature and power design space. This paper demonstrates that with a suitable choice of only 40 hardware-independent program characteristics related to the instruction mix, instruction-level parallelism, control flow behavior, and memory access patterns, it is possible to generate a synthetic benchmark whose performance relates to that of general-purpose and commercial applications. Leveraging this abstract workload modeling approach, we propose StressMaker, a framework that uses machine learning for the automated generation of stressmarks. A comparison with an exhaustive exploration of a large power design space demonstrates that StressMaker is very effective in automatically generating stressmarks in a limited amount of time.

Standard TCP is successful at low speeds but it is unfit for high speed communication due to its slow ramp-up of congestion window size. Many modern fields of study require high bandwidth connectivity. Various solutions to this, including server side modifications of TCP, like HighSpeed TCP and Fast TCP, are being advocated. In this work, we study the effectiveness of these two protocols by comparing and contrasting both of them with standard TCP.