Enabling the Adoption of Processing-in-Memory: Challenges, Mechanisms, Future Research Directions

Abstract

Poor DRAM technology scaling over the course of many years has caused DRAM-based main memory to increasingly become a larger system bottleneck. A major reason for the bottleneck is that data stored within DRAM must be moved across a pin-limited memory channel to the CPU before any computation can take place. This requires a high latency and energy overhead, and the data often cannot benefit from caching in the CPU, making it difficult to amortize the overhead. Modern 3D-stacked DRAM architectures include a logic layer, where compute logic can be integrated underneath multiple layers of DRAM cell arrays within the same chip. Architects can take advantage of the logic layer to perform processing-in-memory (PIM), or near-data processing. In a PIM architecture, the logic layer within DRAM has access to the high internal bandwidth available within 3D-stacked DRAM (which is much greater than the bandwidth available between DRAM and the CPU). Thus, PIM architectures can effectively free up valuable memory channel bandwidth while reducing system energy consumption. A number of important issues arise when we add compute logic to DRAM. In particular, the logic does not have low-latency access to common CPU structures that are essential for modern application execution, such as the virtual memory and cache coherence mechanisms. To ease the widespread adoption of PIM, we ideally would like to maintain traditional virtual memory abstractions and the shared memory programming model. This requires efficient mechanisms that can provide logic in DRAM with access to CPU structures without having to communicate frequently with the CPU. To this end, we propose and evaluate two general-purpose solutions that minimize unnecessary off-chip communication for PIM architectures. We show that both mechanisms improve the performance and energy consumption of many important memory-intensive applications.

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• Benchmarking a New Paradigm: An Experimental Analysis of a Real Processing-in-Memory Architecture
• Enabling Practical Processing in and near Memory for Data-Intensive Computing
• A Workload and Programming Ease Driven Perspective of Processing-in-Memory
• Moving Processing to Data: On the Influence of Processing in Memory on Data Management
• PushdownDB: Accelerating a DBMS using S3 Computation
• Processing Data Where It Makes Sense: Enabling In-Memory Computation
• SIMDRAM: An End-to-End Framework for Bit-Serial SIMD Computing in DRAM
• Data-Centric and Data-Aware Frameworks for Fundamentally Efficient Data Handling in Modern Computing Systems
• Enhancing Programmability, Portability, and Performance with Rich Cross-Layer Abstractions
• Mapping high-performance RNNs to in-memory neuromorphic chips
• D-RaNGe: Using Commodity DRAM Devices to Generate True Random Numbers with Low Latency and High Throughput
• Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management