Energy consumed for transferring data across the processor memory hierarchy constitutes a large fraction of total system energy consumption, and this fraction has steadily increased with technology scaling. In this paper, we propose near-DRAM acceleration (NDA) architectures, which process data using accelerators 3D-stacked on DRAM devices comprising off-chip main memory modules. NDA transfers most data through high-bandwidth and low-energy 3D interconnects between accelerators and DRAM devices instead of low-bandwidth and high-energy off-chip interconnects between a processor and DRAM devices, substantially reducing energy consumption and improving performance. Unlike previous near-memory processing architectures, NDA is built upon commodity DRAM devices; apart from inserting through-silicon vias (TSVs) to 3D-interconnect DRAM devices and accelerators, NDA requires minimal changes to the commodity DRAM device and standard memory module architectures. This allows NDA to be more easily adopted in both existing and emerging systems. Our experiments demonstrate that, on average, our NDA-based system consumes 46% (68%) lower (data transfer) energy at 1.67× higher performance than a system that integrates the same accelerator logic within the processor itself.

NDA: Near-DRAM acceleration architecture leveraging commodity DRAM devices and standard memory modules

While Processing-in-Memory has been investigated for decades, it has not been embraced commercially. A number of emerging technologies have renewed interest in this topic. In particular, the emergence of 3D stacking and the imminent release of Micron's Hybrid Memory Cube device have made it more practical to move computation near memory. However, the literature is missing a detailed analysis of a killer application that can leverage a Near Data Computing (NDC) architecture. This paper focuses on in-memory MapReduce workloads that are commercially important and are especially suitable for NDC because of their embarrassing parallelism and largely localized memory accesses. The NDC architecture incorporates several simple processing cores on a separate, non-memory die in a 3D-stacked memory package; these cores can perform Map operations with efficient memory access and without hitting the bandwidth wall. This paper describes and evaluates a number of key elements necessary in realizing efficient NDC operation: (i) low-EPI cores, (ii) long daisy chains of memory devices, (iii) the dynamic activation of cores and SerDes links. Compared to a baseline that is heavily optimized for MapReduce execution, the NDC design yields up to 15X reduction in execution time and 18X reduction in system energy.

/pdf/ndc-analyzing-the-impact-of-3d-stacked-memory-logic-devices-2504jzf4l6.pdf

NDC: Analyzing the impact of 3D-stacked memory+logic devices on MapReduce workloads

Main memory bandwidth is a critical bottleneck for modern GPU systems due to limited off-chip pin bandwidth. 3D-stacked memory architectures provide a promising opportunity to significantly alleviate this bottleneck by directly connecting a logic layer to the DRAM layers with high bandwidth connections. Recent work has shown promising potential performance benefits from an architecture that connects multiple such 3D-stacked memories and offloads bandwidth-intensive computations to a GPU in each of the logic layers. An unsolved key challenge in such a system is how to enable computation offloading and data mapping to multiple 3D-stacked memories without burdening the programmer such that any application can transparently benefit from near-data processing capabilities in the logic layer.Our paper develops two new mechanisms to address this key challenge. First, a compiler-based technique that automatically identifies code to offload to a logic-layer GPU based on a simple cost-benefit analysis. Second, a software/hardware cooperative mechanism that predicts which memory pages will be accessed by offloaded code, and places those pages in the memory stack closest to the offloaded code, to minimize off-chip bandwidth consumption. We call the combination of these two programmer-transparent mechanisms TOM: Transparent Offloading and Mapping.Our extensive evaluations across a variety of modern memory-intensive GPU workloads show that, without requiring any program modification, TOM significantly improves performance (by 30% on average, and up to 76%) compared to a baseline GPU system that cannot offload computation to 3D-stacked memories.

Transparent offloading and mapping (TOM): enabling programmer-transparent near-data processing in GPU systems

In this paper we focus on common data reorganization operations such as shuffle, pack/unpack, swap, transpose, and layout transformations. Although these operations simply relocate the data in the memory, they are costly on conventional systems mainly due to inefficient access patterns, limited data reuse and roundtrip data traversal throughout the memory hierarchy. This paper presents a two pronged approach for efficient data reorganization, which combines (i) a proposed DRAM-aware reshape accelerator integrated within 3D-stacked DRAM, and (ii) a mathematical framework that is used to represent and optimize the reorganization operations. We evaluate our proposed system through two major use cases. First, we demonstrate the reshape accelerator in performing a physical address remapping via data layout transform to utilize the internal parallelism/locality of the 3D-stacked DRAM structure more efficiently for general purpose workloads. Then, we focus on offloading and accelerating commonly used data reorganization routines selected from the Intel Math Kernel Library package. We evaluate the energy and performance benefits of our approach by comparing it against existing optimized implementations on state-of-the-art GPUs and CPUs. For the various test cases, in-memory data reorganization provides orders of magnitude performance and energy efficiency improvements via low overhead hardware.

/pdf/data-reorganization-in-memory-using-3d-stacked-dram-42hn4a8nop.pdf

Data reorganization in memory using 3D-stacked DRAM

Several recent works have demonstrated the benefits of through-silicon-via (TSV) based 3D integration [1–4], but none of them involves a fully functioning multicore processor and memory stacking. 3D-MAPS (3D Massively Parallel Processor with Stacked Memory) is a two-tier 3D IC, where the logic die consists of 64 general-purpose processor cores running at 277MHz, and the memory die contains 256KB SRAM (see Fig. 10.6.1). Fabrication is done using 130nm GlobalFoundries device technology and Tezzaron TSV and bonding technology. Packaging is done by Amkor. This processor contains 33M transistors, 50K TSVs, and 50K face-to-face connections in 5×5mm2 footprint. The chip runs at 1.5V and consumes up to 4W, resulting in 16W/cm2 power density. The core architecture is developed from scratch to benefit from single-cycle access to SRAM.

3D-MAPS: 3D Massively parallel processor with stacked memory

This paper describes the architecture, design, analysis, and simulation and measurement results of the 3D-MAPS (3D massively parallel processor with stacked memory) chip built with a 1.5 V, 130 nm process technology and a two-tier 3D stacking technology using 1.2 \microm-diameter, 6 \micro m-height through-silicon vias (TSVs) and 3.4\nbsp\microm-diameter face-to-face bond pads. 3D-MAPS consists of a core tier containing 64 cores and a memory tier containing 64 memory blocks. Each core communicates with its dedicated 4KB SRAM block using face-to-face bond pads, which provide negligible data transfer delay between the core and the memory tiers. The maximum operating frequency is 277 MHz and the maximum memory bandwidth is 70.9 GB/s at 277 MHz. The peak measured memory bandwidth usage is 63.8 GB/s and the peak measured power is approximately 4 W based on eight parallel benchmarks.

Design and Analysis of 3D-MAPS (3D Massively Parallel Processor with Stacked Memory)

We describe the design and analysis of 3D-MAPS, a 64-core 3D-stacked memory-on-processor running at 277 MHz with 63 GB/s memory bandwidth, sent for fabrication using Tezzaron's 3D stacking technology. We also describe the design flow used to implement it using industrial 2D tools and custom add-ons to handle 3D specifics.

/pdf/design-and-analysis-of-3d-maps-a-many-core-3d-processor-with-vroq2ab3iw.pdf

Design and analysis of 3D-MAPS: A many-core 3D processor with stacked memory

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3D-MAPS: 3D Massively parallel processor with stacked memory

Design and Analysis of 3D-MAPS (3D Massively Parallel Processor with Stacked Memory)

Design and analysis of 3D-MAPS: A many-core 3D processor with stacked memory