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In-Memory Big Data Management and Processing: A Survey Open
Growing main memory capacity has fueled the development of in-memory big data management and processing. By eliminating disk I/O bottleneck, it is now possible to support interactive data analytics. However, in-memory systems are much more…
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DRISA Open
Data movement between the processing units and the memory in traditional von Neumann architecture is creating the "memory wall" problem. To bridge the gap, two approaches, the memory-rich processor (more on-chip memory) and the compute-cap…
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Basic Performance Measurements of the Intel Optane DC Persistent Memory Module Open
Scalable nonvolatile memory DIMMs will finally be commercially available with the release of the Intel Optane DC Persistent Memory Module (or just "Optane DC PMM"). This new nonvolatile DIMM supports byte-granularity accesses with access t…
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Device and Circuit Architectures for In‐Memory Computing Open
With the rise in artificial intelligence (AI), computing systems are facing new challenges related to the large amount of data and the increasing burden of communication between the memory and the processing unit. In‐memory computing (IMC)…
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Brief Announcement: On Implementing Wear Leveling in Persistent Synchronization Structures Open
The last decade has witnessed an explosion of research on persistent memory, which combines the low access latency of dynamic random access memory (DRAM) with the durability of secondary storage. Intel’s implementation of persistent memory…
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Can far memory improve job throughput? Open
As memory requirements grow, and advances in memory technology slow, the availability of sufficient main memory is increasingly the bottleneck in large compute clusters. One solution to this is memory disaggregation, where jobs can remotel…
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Computational phase-change memory: beyond von Neumann computing Open
The explosive growth in data-centric artificial intelligence related applications necessitates a radical departure from traditional von Neumann computing systems, which involve separate processing and memory units. Computational memory is …
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Neural Map: Structured Memory for Deep Reinforcement Learning Open
A critical component to enabling intelligent reasoning in partially observable environments is memory. Despite this importance, Deep Reinforcement Learning (DRL) agents have so far used relatively simple memory architectures, with the main…
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Rethinking software runtimes for disaggregated memory Open
Disaggregated memory can address resource provisioning inefficiencies in current datacenters. Multiple software runtimes for disaggregated memory have been proposed in an attempt to make disaggregated memory practical. These systems rely o…
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Clio: a hardware-software co-designed disaggregated memory system Open
Memory disaggregation has attracted great attention recently because of its benefits in efficient memory utilization and ease of management. So far, memory disaggregation research has all taken one of two approaches: building/emulating mem…
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A Framework for Memory Oversubscription Management in Graphics Processing Units Open
Modern discrete GPUs support unified memory and demand paging. Automatic management of data movement between CPU memory and GPU memory dramatically reduces developer effort. However, when application working sets exceed physical memory cap…
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An Overview of Processing-in-Memory Circuits for Artificial Intelligence and Machine Learning Open
Artificial intelligence (AI) and machine learning (ML) are revolutionizing many fields of study, such as visual recognition, natural language processing, autonomous vehicles, and prediction. Traditional von-Neumann computing architecture w…
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Performance characterization of a DRAM-NVM hybrid memory architecture for HPC applications using intel optane DC persistent memory modules Open
Non-volatile, byte-addressable memory (NVM) has been introduced by Intel in the form of NVDIMMs named Intel® Optane™ DC PMM. This memory module has the ability to persist the data stored in it without the need for power. This expands the m…
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In-Memory Data Parallel Processor Open
Recent developments in Non-Volatile Memories (NVMs) have opened up a new horizon for in-memory computing. Despite the significant performance gain offered by computational NVMs, previous works have relied on manual mapping of specialized k…
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Panthera: holistic memory management for big data processing over hybrid memories Open
Modern data-parallel systems such as Spark rely increasingly on in-memory computing that can significantly improve the efficiency of iterative algorithms. To process real-world datasets, modern data-parallel systems often require extremely…
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MEMTIS: Efficient Memory Tiering with Dynamic Page Classification and Page Size Determination Open
The evergrowing memory demand fueled by datacenter workloads is the driving force behind new memory technology innovations (e.g., NVM, CXL). Tiered memory is a promising solution which harnesses such multiple memory types with varying capa…
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DeepMem Open
Kernel data structure detection is an important task in memory forensics that aims at identifying semantically important kernel data structures from raw memory dumps. It is primarily used to collect evidence of malicious or criminal behavi…
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Enabling CXL Memory Expansion for In-Memory Database Management Systems Open
Limited memory volume is always a performance bottleneck in an in-memory database management system (IMDBMS) as the data size keeps increasing. To overcome the physical memory limitation, heterogeneous and disaggregated computing platforms…
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Umpire: Application-focused management and coordination of complex hierarchical memory Open
Advanced architectures like Sierra provide a wide range of memory resources that must often be carefully controlled by the user. These resources have varying capacities, access timing rules, and visibility to different compute resources. A…
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Near memory data structure rearrangement Open
As CPU core counts continue to increase, the gap between compute power and available memory bandwidth has widened. A larger and deeper cache hierarchy benefits locality-friendly computation, but offers limited improvement to irregular, dat…
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RACER: Bit-Pipelined Processing Using Resistive Memory Open
To combat the high energy costs of moving data between main memory and the CPU, recent works have proposed to perform processing-using-memory (PUM), a type of processing-in-memory where operations are performed on data in situ (i.e., right…
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Smart Memory Management (SaMM) For Embedded Systems without MMU Open
In the wake of extensible usage of IOT (Internet of Things) enabled Embedded Systems, it is of great importance to find ways of using Memory in the most efficient way. Embedded Systems are also space constrained, hence at various places, i…
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Effectively Prefetching Remote Memory with Leap Open
Memory disaggregation over RDMA can improve the performance of memory-constrained applications by replacing disk swapping with remote memory accesses. However, state-of-the-art memory disaggregation solutions still use data path components…
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Memory Sizing of a Scalable SRAM In-Memory Computing Tile Based Architecture Open
Modern computing applications require more and more data to be processed. Unfortunately, the trend in memory technologies does not scale as fast as the computing performances, leading to the so called memory wall. New architectures are cur…
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Disaggregated Cloud Memory with Elastic Block Management Open
With the growing importance of in-memory data processing, cloud service providers have launched large memory virtual machine services to accommodate memory intensive workloads. Such large memory services using low volume scaled-up machines…
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Deforming the metric of cognitive maps distorts memory Open
Environmental boundaries anchor cognitive maps that support memory. However, trapezoidal boundary geometry distorts the regular firing patterns of entorhinal grid cells proposedly providing a metric for cognitive maps. Here, we test the im…
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Fast, multicore-scalable, low-fragmentation memory allocation through large virtual memory and global data structures Open
We demonstrate that general-purpose memory allocation involving many threads\non many cores can be done with high performance, multicore scalability, and low\nmemory consumption. For this purpose, we have designed and implemented scalloc,\…
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Recurrent Memory Transformer Open
Transformer-based models show their effectiveness across multiple domains and tasks. The self-attention allows to combine information from all sequence elements into context-aware representations. However, global and local information has …
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Secure Page Fusion with VUsion Open
To reduce memory pressure, modern operating systems and hypervisors such as Linux/KVM deploy page-level memory fusion to merge physical memory pages with the same content (i.e., page fusion). A write to a fused memory page triggers a copy-…
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Larger-than-memory data management on modern storage hardware for in-memory OLTP database systems Open
In-memory database management systems (DBMSs) outperform disk-oriented systems for on-line transaction processing (OLTP) workloads. But this improved performance is only achievable when the database is smaller than the amount of physical m…