Summary Pathfinding Future PIM Architectures Demystifying Commercial PIM Technology arxiv.org
13,427 words - PDF document - View PDF document
One Line
The document explores the use of PIM architectures, focusing on the UPMEM-PIM programming model that facilitates data sharing and synchronization between threads within the same system.
Slides
Slide Presentation (10 slides)
Key Points
- PIM (Processing-in-memory) architectures have been researched for decades but have not been widely implemented due to various limitations.
- UPMEM-PIM follows the single-program multiple-data (SPMD) paradigm, allowing for data sharing among threads executing within the same DPU.
- PIMulator is a simulation framework that supports the execution-driven simulation of UPMEM ISA-compatible instructions.
- The performance of PrIM benchmarks showed compute-bound behavior and fluctuating thread-level parallelism during execution.
- Optimizing the PIM memory system is crucial for future SIM architectures.
- Current commercial PIM chips face limitations in multi-tenancy, preventing secure execution of co-located workloads.
- Cache-centric PIM architecture offers performance benefits compared to scratchpad-centric design.
- Various research papers and conference proceedings cover topics such as intelligent RAM, computation models, practical challenges, and accelerators for PIM technology.
Summaries
28 word summary
This document examines the implementation of PIM architectures in computer systems, specifically the UPMEM-PIM programming model which enables data sharing and synchronization among threads within the same D.
34 word summary
The document explores the implementation of Processing-in-memory (PIM) architectures in computer systems. The UPMEM-PIM programming model follows the single-program multiple-data (SPMD) paradigm, allowing for data sharing and synchronization among threads within the same D
596 word summary
This summary will highlight the key points from the excerpted text.
The document discusses the exploration of Processing-in-memory (PIM) architectures in computer systems. While PIM has been researched for decades, it has not been widely implemented due to high
The UPMEM-PIM programming model follows the single-program multiple-data (SPMD) paradigm. Programmers write a single program that is executed by all software threads (tasklets), but each thread can have its own control flow and access different parts
UPMEM's scratchpad-centric programming model allows for data sharing and synchronization among threads executing within the same DPU. However, threads executing in different DPUs cannot directly share data or synchronize with each other. To enable data sharing or synchronization across different DP
PIMulator is a simulation framework that supports the execution-driven simulation of UPMEM ISA-compatible, machine-level instructions. It consists of a compiler toolchain and a hardware performance simulator. The compiler toolchain utilizes UPMEM SDK's preprocessor and
The document discusses the Pathfinding Future PIM Architectures and aims to demystify commercial PIM technology. It mentions various datasets, elements, and queries used in the study. The document also discusses the utilization of arbitrary locations in the memory address space
PrIM's compute utilization and memory read bandwidth utilization are shown in Figure 5. The maximum DRAM bandwidth in a real UPMEM-PIM system is around 600 MB/sec. PrIM targets data-intensive workloads and shifts the performance bottleneck
The runtime performance of PrIM benchmarks was analyzed, showing that most benchmarks have a compute-bound behavior. The number of issuable threads and the instruction mix were also examined. PIMulator was used to analyze how thread-level parallelism fluctuates during execution
The baseline UPMEM-PIM architecture employs a scalar processor with thread-level parallelism. However, its memory system does not meet the requirements for vector execution, resulting in limited speedup. Optimizing the PIM memory system is crucial for future SIM
Current commercial PIM chips, including UPMEM-PIM, are not able to meet the requirements of multi-tenancy due to limitations in hardware/software and programming models. Co-located workloads cannot securely execute without interfering with each other, and
The performance overhead of adding address translations to a scratchpad-centric PIM architecture is low, with an average loss of 0.8% and a maximum loss of 14.1%. This is due to the scratchpad-centric memory model's high
This excerpt discusses the performance and benefits of a cache-centric PIM architecture compared to a scratchpad-centric design. The PIMulator framework is introduced, which emulates the cache-centric UPMEM-PIM by directly allocating input data in the WRAM
This summary provides a list of references to various research papers and conference proceedings related to Processing-in-Memory (PIM) technology. These references cover topics such as intelligent RAM, computation models for intelligent memory, the architecture of PIM chips, practical challenges
This excerpt includes a list of references to various research papers and articles related to the topic of future PIM architectures. Some key points include the use of in-memory processing in HBM2-PIM and LPDDR5-PIM technologies, the development of
This text excerpt contains a list of references to various research papers, conference proceedings, and technical documents related to Processing-in-Memory (PIM) architectures and accelerators. The references cover a range of topics including DRAM-based accelerators for CNN inference
This excerpt includes a list of references to various research papers and documents related to PIM (Processing-In-Memory) architectures and technologies. The references cover a range of topics including GPU simulation, memory access scheduling, software developer's manuals, benchmarks, managing
This excerpt is a list of references to various papers and articles related to processing-in-memory (PIM) architectures. The references include papers that discuss topics such as efficient synchronization support for near-data-processing architectures, resource management of latency-critical applications in clouds