Enabling In-Network Floating-Point Arithmetic for Efficient Computation Offloading

Cui, Penglai1,2; Pan, Heng1,3; Li, Zhenyu1,3; Zhang, Penghao1,2; Miao, Tianhao1,2; Zhou, Jianer4; Guan, Hongtao1; Xie, Gaogang5

2022-12-01

10.1109/TPDS.2022.3208425

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS   影响因子和分区

1045-9219

1558-2183

Programmable switches are recently used for accelerating data-intensive distributed applications. Some computational tasks, traditionally performed on servers in data centers, are offloaded into the network on programmable switches. These tasks may require the support of on-the-fly floating-point operations. Unfortunately, programmable switches are restricted to simple integer arithmetic operations. Existing systems circumvent this restriction by converting floats to integers or relying on local CPUs of switches, incurring extra processing delayed and accuracy loss. To address this gap, we propose NetFC, a table-lookup method to achieve on-the-fly in-network floating-point arithmetic operations nearly without accuracy loss. Specifically, NetFC utilizes logarithm projection and transformation to convert the original huge table enumerating all operands and results into several much smaller tables that can fit into the data plane of programmable switches. To cope with the table inflation problem on 32-bit floats, we also propose an approximation method that further breaks the large tables into smaller ones. In addition, NetFC leverages two optimizations to improve accuracy and reduce on-chip memory consumption. We use both synthetic and real-life datasets to evaluate NetFC. The experimental results show that the average accuracy of NetFC is above 99.9% with only 448KB memory consumption for 16-bit floats and 99.1% with 496KB memory consumption for 32-bit floats. Furthermore, we integrate NetFC into two distributed applications and two in-network telemetry systems to show its effectiveness in further improving the performance.

Open area test sites Arithmetic Memory management Task analysis Training Standards Servers In-network computation computation offloading floating-point operation

SCI

英语

其他

National Key R&D Program of China[2020YFB1805600] ; National Natural Science Foundation of China["U20A20180","62002344"] ; Beijing Natural Science Foundation[JQ20024]

Computer Science ; Engineering

Computer Science, Theory & Methods ; Engineering, Electrical & Electronic

WOS:000864178200003

IEEE COMPUTER SOC

COMPUTER SCIENCE

Web of Science

1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Purple Mt Labs, Nanjing 211111, Peoples R China
4.Southern Univ Sci & Technol, Shenzhen 518055, Peoples R China
5.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China

Cui, Penglai,Pan, Heng,Li, Zhenyu,et al. Enabling In-Network Floating-Point Arithmetic for Efficient Computation Offloading[J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,2022,33(12):4918-4934.

Cui, Penglai.,Pan, Heng.,Li, Zhenyu.,Zhang, Penghao.,Miao, Tianhao.,...&Xie, Gaogang.(2022).Enabling In-Network Floating-Point Arithmetic for Efficient Computation Offloading.IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,33(12),4918-4934.

Cui, Penglai,et al."Enabling In-Network Floating-Point Arithmetic for Efficient Computation Offloading".IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 33.12(2022):4918-4934.