Using PIM to overcome memory bottleneck • Although various bandwidth increase methods have been proposed, it is physically impossible to achieve a breakthrough increase. - Limited by # of PCB wires, # of CPU ball, and thermal constraints • PIM has been proposed to improve performance of bandwidth-intensive workloads and improve energy efficiency by reducing computing-memory data movement.

Emerging applications such as deep neural network demand high off-chip memory bandwidth. However, under stringent physical constraints of chip packages and system boards, it becomes very expensive to further increase the bandwidth of off-chip memory. Besides, transferring data across the memory hierarchy constitutes a large fraction of total energy consumption of systems, and the fraction has steadily increased with the stagnant technology scaling and poor data reuse characteristics of such emerging applications. To cost-effectively increase the bandwidth and energy efficiency, researchers began to reconsider the past processing-in-memory (PIM) architectures and advance them further, especially exploiting recent integration technologies such as 2.5D/3D stacking. Albeit the recent advances, no major memory manufacturer has developed even a proof-of-concept silicon yet, not to mention a product. This is because the past PIM architectures often require changes in host processors and/or application code which memory manufacturers cannot easily govern. In this paper, elegantly tackling the aforementioned challenges, we propose an innovative yet practical PIM architecture. To demonstrate its practicality and effectiveness at the system level, we implement it with a 20nm DRAM technology, integrate it with an unmodified commercial processor, develop the necessary software stack, and run existing applications without changing their source code. Our evaluation at the system level shows that our PIM improves the performance of memory-bound neural network kernels and applications by 11.2× and 3.5×, respectively. Atop the performance improvement, PIM also reduces the energy per bit transfer by 3.5×, and the overall energy efficiency of the system running the applications by 3.2×.

Performance gains in memory have traditionally been obtained by increasing memory bus widths and speeds. The diminishing returns of such techniques have led to the proposal of an alternate architecture, the fully-buffered DIMM. This new standard replaces the conventional memory bus with a narrow, high-speed interface between the memory controller and the DIMMs. This paper examines how traditional DDRx based memory controller policies for scheduling and row buffer management perform on a fully-buffered DIMM memory architecture. The split-bus architecture used by FBDIMM systems results in an average improvement of 7% in latency and 10% in bandwidth at higher utilizations. On the other hand, at lower utilizations, the increased cost of serialization resulted in a degradation in latency and bandwidth of 25% and 10% respectively. The split-bus architecture also makes the system performance sensitive to the ratio of read and write traffic in the workload. In larger configurations, we found that the FBDIMM system performance was more sensitive to usage of the FBDIMM links than to DRAM bank availability. In general, FBDIMM performance is similar to that of DDRx systems, and provides better performance characteristics at higher utilization, making it a relatively inexpensive mechanism for scaling capacity at higher bandwidth requirements. The mechanism is also largely insensitive to scheduling policies, provided certain ground rules are obeyed

We describe ELVIS (the Ecosystem Location Visualization and Information System), a suite of tools for constructing food webs for a given location. We express both ELVIS input and output data in OWL, thereby enabling its integration with other semantic web resources. In particular, we describe using a Triple Shop application to answer SPARQL queries from a collection of semantic web documents. This is an end-to-end case study of the semantic web's utility for ecological and environmental research.