Exploring the Efficiency of Data-Oblivious Programs

• Published in: 2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) pp. 189 - 200
• Main Authors: Biernacki, Lauren, Tiruye, Biniyam Mengist, Demissie, Meron Zerihun, Andargie, Fitsum Assamnew, Reagen, Brandon, Austin, Todd
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2023
• Subjects: Benchmark testing; Data oblivious programming; If conversion
• DOI: 10.1109/ISPASS57527.2023.00027

Data-oblivious programs have gained popularity due to their application in security, but are often dismissed because of anticipated performance loss. In order to better understand these performance concerns, this paper details the first performance characterization of data-oblivious programs. We study mechanical data-oblivious transformations applied to twenty workloads from the VIP-Bench benchmark suite and find that, overall, performance overheads vary widely, with a geomean slowdown of 7.4×. This variance can be attributed to whether or not the data-oblivious transformations affect the workload's asymptotic complexity. Performance overheads are much lower for the fourteen workloads whose complexity is unaffected, at 1.9× geomean. Further, by reducing control hazards, we find that dataoblivious transformations often result in improved per-instruction performance (e.g., better branch and memory performance) and increase the number of instructions the processor can execute in parallel (e.g., IPC). Leveraging lessons from analyzing these overheads, we study four notably slow data-oblivious workloads and show how algorithmic changes can significantly improve performance-achieving an average 86.4× speedup over the mechanically produced baseline programs. While data-oblivious program execution often incurs overheads, the contributions of this paper show that these overheads can be overcome by compiler and algorithmic optimizations, bringing us closer to achieving efficient and widely-used data-oblivious programs.