TY - GEN
T1 - CXL Memory as Persistent Memory for Disaggregated HPC
T2 - 2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023
AU - Fridman, Yehonatan
AU - Mutalik Desai, Suprasad
AU - Singh, Navneet
AU - Willhalm, Thomas
AU - Oren, Gal
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/11/12
Y1 - 2023/11/12
N2 - In the landscape of High-Performance Computing (HPC), the quest for efficient and scalable memory solutions remains paramount. The advent of Compute Express Link (CXL) introduces a promising avenue with its potential to function as a Persistent Memory (PMem) solution in the context of disaggregated HPC systems. This paper presents a comprehensive exploration of CXL memory's viability as a candidate for PMem, supported by physical experiments conducted on cutting-edge multi-NUMA nodes equipped with CXL-attached memory prototypes. Our study not only benchmarks the performance of CXL memory but also illustrates the seamless transition from traditional PMem programming models to CXL, reinforcing its practicality. To substantiate our claims, we establish a tangible CXL prototype using an FPGA card embodying CXL 1.1/2.0 compliant endpoint designs (Intel FPGA CXL IP). Performance evaluations, executed through the STREAM and STREAM-PMem benchmarks, showcase CXL memory's ability to mirror PMem characteristics in App-Direct and Memory Mode while achieving impressive bandwidth metrics with Intel 4th generation Xeon (Sapphire Rapids) processors. The results elucidate the feasibility of CXL memory as a persistent memory solution, outperforming previously established benchmarks. In contrast to published DCPMM results, our CXL-DDR4 memory module offers comparable bandwidth to local DDR4 memory configurations, albeit with a moderate decrease in performance. The modified STREAM-PMem application underscores the ease of transitioning programming models from PMem to CXL, thus underscoring the practicality of adopting CXL memory. The sources of this work are available at: https://github.com/Scientific-Computing-Lab-NRCN/STREAMer.
AB - In the landscape of High-Performance Computing (HPC), the quest for efficient and scalable memory solutions remains paramount. The advent of Compute Express Link (CXL) introduces a promising avenue with its potential to function as a Persistent Memory (PMem) solution in the context of disaggregated HPC systems. This paper presents a comprehensive exploration of CXL memory's viability as a candidate for PMem, supported by physical experiments conducted on cutting-edge multi-NUMA nodes equipped with CXL-attached memory prototypes. Our study not only benchmarks the performance of CXL memory but also illustrates the seamless transition from traditional PMem programming models to CXL, reinforcing its practicality. To substantiate our claims, we establish a tangible CXL prototype using an FPGA card embodying CXL 1.1/2.0 compliant endpoint designs (Intel FPGA CXL IP). Performance evaluations, executed through the STREAM and STREAM-PMem benchmarks, showcase CXL memory's ability to mirror PMem characteristics in App-Direct and Memory Mode while achieving impressive bandwidth metrics with Intel 4th generation Xeon (Sapphire Rapids) processors. The results elucidate the feasibility of CXL memory as a persistent memory solution, outperforming previously established benchmarks. In contrast to published DCPMM results, our CXL-DDR4 memory module offers comparable bandwidth to local DDR4 memory configurations, albeit with a moderate decrease in performance. The modified STREAM-PMem application underscores the ease of transitioning programming models from PMem to CXL, thus underscoring the practicality of adopting CXL memory. The sources of this work are available at: https://github.com/Scientific-Computing-Lab-NRCN/STREAMer.
KW - CXL
KW - HPC
KW - Intel Optane DCPMM
KW - Memory disaggregation
KW - Persistent Memory (PMem)
KW - STREAM
KW - STREAM-PMem
KW - STREAMer
UR - http://www.scopus.com/inward/record.url?scp=85178145517&partnerID=8YFLogxK
U2 - 10.1145/3624062.3624175
DO - 10.1145/3624062.3624175
M3 - Conference contribution
AN - SCOPUS:85178145517
T3 - ACM International Conference Proceeding Series
SP - 983
EP - 994
BT - Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023
PB - Association for Computing Machinery
Y2 - 12 November 2023 through 17 November 2023
ER -