Memory virtualization

Summary

In computer science, memory virtualization decouples volatile random access memory (RAM) resources from individual systems in the data centre, and then aggregates those resources into a virtualized memory pool available to any computer in the cluster. The memory pool is accessed by the operating system or applications running on top of the operating system. The distributed memory pool can then be utilized as a high-speed cache, a messaging layer, or a large, shared memory resource for a CPU or a GPU application. Description Memory virtualization allows networked, and therefore distributed, servers to share a pool of memory to overcome physical memory limitations, a common bottleneck in software performance. With this capability integrated into the network, applications can take advantage of a very large amount of memory to improve overall performance, system utilization, increase memory usage efficiency, and enable new use cases. Software on the memory pool nodes (server

Official source

https://en.wikipedia.org/wiki/Memory_virtualization

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Emerging datacenter applications operate on vast datasets that are kept in DRAM to minimize latency. The large number of servers needed to accommodate this massive memory footprint requires frequent server-to-server communication in applications such as key-value stores and graph-based applications that rely on large irregular data structures. The fine-grained nature of the accesses is a poor match to commodity networking technologies, including RDMA, which incur delays of 10-1000x over local DRAM operations. We introduce Scale-Out NUMA (soNUMA) – an architecture, programming model, and communication protocol for low-latency, distributed in-memory processing. soNUMA layers an RDMA-inspired programming model directly on top of a NUMA memory fabric via a stateless messaging protocol. To facilitate interactions between the application, OS, and the fabric, soNUMA relies on the remote memory controller – a new architecturally-exposed hardware block integrated into the node’s local coherence hierarchy. Our results based on cycle-accurate full-system simulation show that soNUMA performs remote reads at latencies that are within 4x of local DRAM, can fully utilize the available memory bandwidth, and can issue up to 10M remote memory operations per second per core.

2014

Selective, accurate, and timely self-invalidation using last-touch prediction

Babak Falsafi

Communication in cache-coherent distributed shared memory (DSM) often requires invalidating (or writing back) cached copies of a memory block, incurring high overheads. This paper proposes Last-Touch Predictors (LTPs) that learn and predict the “last touch” to a memory block by one processor before the block is accessed and subsequently invalidated by another. By predicting a last-touch and (self-)invalidating the block in advance, an LTP hides the invalidation time, significantly reducing the coherence overhead. The key behind accurate last-touch prediction is trace-based correlation, associating a last-touch with the sequence of instructions (i.e. a trace) touching the block from a coherence miss until the block is invalidated. Correlating instructions enables an LTP to identify a last-touch to a memory block uniquely throughout an application's execution. In this paper we use results from running shared-memory applications on a simulated DSM to evaluate LTPs. The results indicate that: (1) our base case LTP design maintaining trace signatures on a per-block basis, substantially improves prediction accuracy over previous self-invalidation schemes to an average of 79%; (2) our alternative LTP design, maintaining a global trace signature table, reduces storage overhead but only achieves an average accuracy of 58%; (3) last-touch prediction based on a single instruction only achieves an average accuracy of 41% due to instruction reuse within and across computation; and (4) LTP enables selective, accurate, and timely self- invalidation in DSM, speeding up program execution on average by 11%

2000