CAREER: Hardware-Software Co-Design to Dynamically Specialize the Memory Hierarchy

Decades of exponential growth in computing power have yielded transformative benefits for research, industry, and society. To sustain this growth as Dennard Scaling benefits fade, computer architects must design new systems with orders-of-magnitude better performance and energy-efficiency. Unfortunately, computer systems are increasingly limited by the rising cost of accessing data, which often dominates the time and energy spent doing actual computation. This project will design and evaluate a new computer system design that makes data accesses much faster and cheaper by dynamically specializing the memory hierarchy for each application. This project will continue the growth in computation power that researchers, industry, and society have come to rely upon. It will particularly accelerate important applications like machine learning, social networking, and robotics whose core computations remain beyond the capability of current chip designs. This project will develop new curricula on memory hierarchy and specialized architectures as well as involve high school, undergraduate, and graduate students in research. It will improve diversity through research workshops for undergraduate women and a summer internship program for under-represented minorities.

Current computer systems incur significant unnecessary data movement because their memory hierarchy is fixed in hardware and hidden from software, so that applications have no control over how data are managed. This project will develop a new hardware-software co-design that incorporates data as a first-class citizen alongside compute. Applications will express their performance goals (e.g., throughput, quality of service, and/or security) and, with compiler support, break their computation into tasks with associated data. The operating system (OS) and hardware will collaboratively co-schedule tasks and data so that applications’ goals are achieved with minimal data movement. The OS scheduler will account for the diverse performance characteristics of each core/accelerator in a heterogeneous system-on-chip (SoC), and hardware will incorporate energy-efficient cores throughout the memory hierarchy to enable near-data computation while maximally exploiting data locality. The hardware-software co-design gives an extensible platform for further research on reducing data movement, and it complements industry investment in heterogeneous SoCs by making it inexpensive to integrate new accelerators.

See NSF Award CCF-1845986.

Sponsor

National Science Foundation

Projects

Polymorphic Cache Hierarchy

Software-Defined Cache Hierarchy for Multicore Processors

Publications

Leviathan: A Unified System for General-Purpose Near-Data Computing [pdf]

Brian Schwedock, Nathan Beckmann. MICRO 2024.

UDIR: Towards a Unified Compiler Framework for Reconfigurable Dataflow Architectures [pdf]

Nikhil Agarwal, Mitchell Fream, Souradip Ghosh, Brian Schwedock, Nathan Beckmann. IEEE CAL 2023.

UDIR: Towards a Unified Compiler Framework for Reconfigurable Dataflow Architectures [pdf]

Nikhil Agarwal, Mitchell Fream, Souradip Ghosh, Brian Schwedock, Nathan Beckmann. WDDSA at MICRO 2023.

Affinity Alloc: Taming Not-So-Near Data Computing [pdf]

Zhengrong Wang, Christopher Liu, Nathan Beckmann, Tony Nowatzki. MICRO 2023.

Kobold: Simplified Cache Coherence for Cache-Attached Accelerators [pdf]

Jennifer Brana, Brian Schwedock, Yatin Manerkar, Nathan Beckmann. IEEE CAL 2023.

Kobold: Simplified Cache Coherence for Cache-Attached Accelerators [pdf]

Jennifer Brana, Brian Schwedock, Yatin Manerkar, Nathan Beckmann. WDDSA at MICRO 2022.

täkō: A Polymorphic Cache Hierarchy for General-Purpose Optimization of Data Movement [pdf]

Brian Schwedock, Piratach Yoovidhya, Jennifer Seibert, Nathan Beckmann. ISCA 2022.

Jumanji: The Case for Dynamic NUCA in the Datacenter [pdf]

Brian Schwedock, Nathan Beckmann. MICRO 2020.

Tvarak: Software-Managed Hardware Offload for DAX NVM Storage Redundancy [pdf]

Rajat Kateja, Nathan Beckmann, Gregory R. Ganger. ISCA 2020.

Livia: Data-Centric Computing Throughout the Memory Hierarchy [pdf]

Elliot Lockerman, Axel Feldmann, Mohammad Bakhshalipour, Alexandru Stanescu, Shashwat Gupta, Daniel Sanchez, Nathan Beckmann. ASPLOS 2020.

PHI: Architectural Support for Synchronization- and Bandwidth-Efficient Commutative Scatter Updates [pdf]

Anurag Mukkara, Nathan Beckmann, Daniel Sanchez. MICRO 2019.