In this paper we present DRAMSim2, a cycle accurate memory system simulator. The goal of DRAMSim2 is to be an accurate and publicly available DDR2/3 memory system model which can be used in both full system and trace-based simulations. We describe the process of validating DRAMSim2 timing against manufacturer Verilog models in an effort to prove the accuracy of simulation results. We outline the combination of DRAMSim2 with a cycle-accurate x86 simulator that can be used to perform full system simulations. Finally, we discuss DRAMVis, a visualization tool that can be used to graph and compare the results of DRAMSim2 simulations.

DRAMSim2: A Cycle Accurate Memory System Simulator

Recently, both industry and academia have proposed many different roadmaps for the future of DRAM. Consequently, there is a growing need for an extensible DRAM simulator, which can be easily modified to judge the merits of today's DRAM standards as well as those of tomorrow. In this paper, we present Ramulator , a fast and cycle-accurate DRAM simulator that is built from the ground up for extensibility. Unlike existing simulators, Ramulator is based on a generalized template for modeling a DRAM system, which is only later infused with the specific details of a DRAM standard. Thanks to such a decoupled and modular design, Ramulator is able to provide out-of-the-box support for a wide array of DRAM standards: DDR3/4, LPDDR3/4, GDDR5, WIO1/2, HBM, as well as some academic proposals (SALP, AL-DRAM, TL-DRAM, RowClone, and SARP). Importantly, Ramulator does not sacrifice simulation speed to gain extensibility: according to our evaluations, Ramulator is 2.5 $\times$ faster than the next fastest simulator. Ramulator is released under the permissive BSD license.

/pdf/ramulator-a-fast-and-extensible-dram-simulator-31j8vxv1az.pdf

Ramulator: A Fast and Extensible DRAM Simulator

Dynamic random-access memory (DRAM) is the building block of modern main memory systems. DRAM cells must be periodically refreshed to prevent loss of data. These refresh operations waste energy and degrade system performance by interfering with memory accesses. The negative effects of DRAM refresh increase as DRAM device capacity increases. Existing DRAM devices refresh all cells at a rate determined by the leakiest cell in the device. However, most DRAM cells can retain data for significantly longer. Therefore, many of these refreshes are unnecessary. In this paper, we propose RAIDR (Retention-Aware Intelligent DRAM Refresh), a low-cost mechanism that can identify and skip unnecessary refreshes using knowledge of cell retention times. Our key idea is to group DRAM rows into retention time bins and apply a different refresh rate to each bin. As a result, rows containing leaky cells are refreshed as frequently as normal, while most rows are refreshed less frequently. RAIDR uses Bloom filters to efficiently implement retention time bins. RAIDR requires no modification to DRAM and minimal modification to the memory controller. In an 8-core system with 32 GB DRAM, RAIDR achieves a 74.6% refresh reduction, an average DRAM power reduction of 16.1%, and an average system performance improvement of 8.6% over existing systems, at a modest storage overhead of 1.25 KB in the memory controller. RAIDR's benefits are robust to variation in DRAM system configuration, and increase as memory capacity increases.

/pdf/raidr-retention-aware-intelligent-dram-refresh-4t380u711e.pdf

RAIDR: Retention-Aware Intelligent DRAM Refresh

Architectural simulation is time-consuming, and the trend towards hundreds of cores is making sequential simulation even slower. Existing parallel simulation techniques either scale poorly due to excessive synchronization, or sacrifice accuracy by allowing event reordering and using simplistic contention models. As a result, most researchers use sequential simulators and model small-scale systems with 16-32 cores. With 100-core chips already available, developing simulators that scale to thousands of cores is crucial.We present three novel techniques that, together, make thousand-core simulation practical. First, we speed up detailed core models (including OOO cores) with instruction-driven timing models that leverage dynamic binary translation. Second, we introduce bound-weave, a two-phase parallelization technique that scales parallel simulation on multicore hosts efficiently with minimal loss of accuracy. Third, we implement lightweight user-level virtualization to support complex workloads, including multiprogrammed, client-server, and managed-runtime applications, without the need for full-system simulation, sidestepping the lack of scalable OSs and ISAs that support thousands of cores.We use these techniques to build zsim, a fast, scalable, and accurate simulator. On a 16-core host, zsim models a 1024-core chip at speeds of up to 1,500 MIPS using simple cores and up to 300 MIPS using detailed OOO cores, 2-3 orders of magnitude faster than existing parallel simulators. Simulator performance scales well with both the number of modeled cores and the number of host cores. We validate zsim against a real Westmere system on a wide variety of workloads, and find performance and microarchitectural events to be within a narrow range of the real system.

/pdf/zsim-fast-and-accurate-microarchitectural-simulation-of-1ry6jv61ts.pdf

ZSim: fast and accurate microarchitectural simulation of thousand-core systems

Processing-in-memory (PIM) is rapidly rising as a viable solution for the memory wall crisis, rebounding from its unsuccessful attempts in 1990s due to practicality concerns, which are alleviated with recent advances in 3D stacking technologies. However, it is still challenging to integrate the PIM architectures with existing systems in a seamless manner due to two common characteristics: unconventional programming models for in-memory computation units and lack of ability to utilize large on-chip caches. In this paper, we propose a new PIM architecture that (1) does not change the existing sequential programming models and (2) automatically decides whether to execute PIM operations in memory or processors depending on the locality of data. The key idea is to implement simple in-memory computation using compute-capable memory commands and use specialized instructions, which we call PIM-enabled instructions, to invoke in-memory computation. This allows PIM operations to be interoperable with existing programming models, cache coherence protocols, and virtual memory mechanisms with no modification. In addition, we introduce a simple hardware structure that monitors the locality of data accessed by a PIM-enabled instruction at runtime to adaptively execute the instruction at the host processor (instead of in memory) when the instruction can benefit from large on-chip caches. Consequently, our architecture provides the illusion that PIM operations are executed as if they were host processor instructions. We provide a case study of how ten emerging data-intensive workloads can benefit from our new PIM abstraction and its hardware implementation. Evaluations show that our architecture significantly improves system performance and, more importantly, combines the best parts of conventional and PIM architectures by adapting to data locality of applications.

/pdf/pim-enabled-instructions-a-low-overhead-locality-aware-4gcijahtny.pdf

PIM-enabled instructions: a low-overhead, locality-aware processing-in-memory architecture

As supercomputers grow, understanding their behavior and performance has become increasingly challenging. New hurdles in scalability, programmability, power consumption, reliability, cost, and cooling are emerging, along with new technologies such as 3D integration, GP-GPUs, silicon-photonics, and other "game changers". Currently, they HPC community lacks a unified toolset to evaluate these technologies and design for these challenges.To address this problem, a number of institutions have joined together to create the Structural Simulation Toolkit (SST), an open, modular, parallel, multi-criteria, multi-scale simulation framework. The SST includes a number of processor, memory, and network models. The SST has been used in a variety of network, memory, and application studies and aims to become the standard simulation framework for designing and procuring HPC systems.

/pdf/the-structural-simulation-toolkit-3unhj1dob0.pdf

The structural simulation toolkit

Large last-level caches (L3Cs) are frequently used to bridge the performance and power gap between processor and memory. Although traditional processors implement caches as SRAMs, technologies such as STT-RAM (MRAM), and eDRAM have been used and/or considered for the implementation of L3Cs. Each of these technologies has inherent weaknesses: SRAM is relatively low density and has high leakage current; STT-RAM has high write latency and write energy consumption; and eDRAM requires refresh operations. As future processors are expected to have larger last-level caches, the goal of this paper is to study the trade-offs associated with using each of these technologies to implement L3Cs. In order to make useful comparisons between SRAM, STTRAM, and eDRAM L3Cs, we model them in detail and apply low power techniques to each of these technologies to address their respective weaknesses. We optimize SRAM for low leakage and optimize STT-RAM for low write energy. Moreover, we classify eDRAM refresh-reduction schemes into two categories and demonstrate the effectiveness of using dead-line prediction to eliminate unnecessary refreshes. A comparison of these technologies through full-system simulation shows that the proposed refresh-reduction method makes eDRAM a viable, energy-efficient technology for implementing L3Cs.

https://terpconnect.umd.edu/~blj/papers/hpca2013.pdf

Technology comparison for large last-level caches (L 3 Cs): Low-leakage SRAM, low write-energy STT-RAM, and refresh-optimized eDRAM

The design and implementation of the commodity memory architecture has resulted in significant performance and capacity limitations. To circumvent these limitations, designers and vendors have begun to place intermediate logic between the CPU and DRAM. This additional logic has two functions: to control the DRAM and to communicate with the CPU over a fast and narrow bus. The benefit provided by this logic is a reduction in pin-out to the memory system and increased signal integrity to the DRAM, allowing faster clock rates while maintaining capacity. While the few vendors utilizing this design have used the same general approach, their implementations vary greatly in their non-trivial details. A hardware-verified simulation suite is developed to accurately model and evaluate the behavior of this buffer-on-board memory system. A study of this design space is used to determine optimal use of the resources involved. This includes DRAM and bus organization, queue storage, and mapping schemes. Various constraints based on implementation costs are placed on simulated configurations to confirm that these optimizations apply to viable systems. Finally, full system simulations are performed to better understand how this memory system interacts with an operating system executing an application with the goal of uncovering behaviors not present in simple limit case simulations. When applying insights gleaned from these simulations, optimal performance can be achieved while still considering outside constraints (i.e., pin-out, power, and fabrication costs).

https://terpconnect.umd.edu/~blj/papers/isca2012.pdf

Buffer-on-board memory systems

https://www.cs.umd.edu/~ben/papers/Sopan2012Community.pdf

Paul Rosenfeld

Papers

DRAMSim2: A Cycle Accurate Memory System Simulator

The structural simulation toolkit

Technology comparison for large last-level caches (L 3 Cs): Low-leakage SRAM, low write-energy STT-RAM, and refresh-optimized eDRAM

Buffer-on-board memory systems

Community Health Map: A geospatial and multivariate data visualization tool for public health datasets