eBPF lets developers extend Linux with custom packet processing, tracing, and scheduling logic, and a verifier proves before execution that the code will not crash the kernel. The programming model, however, is fragmented: a single application spans kernel code, a userspace loader, and shared maps, yet the relationships among these pieces go unchecked. E.g. A map or event type defined differently on each side silently corrupts shared state. We observe that these cross-boundary relationships duplicate information that a type system can unify. We present KernelScript, a DSL that types maps, program handles, and execution domains in one source, then compiles to standard C through the original toolchain. We evaluate KernelScript on 43 eBPF workloads covering XDP, TC, kprobe, tracepoint, and struct_ops. KernelScript rejects cross-boundary bugs at compile time that standard C/libbpf still builds and loads, a unified source shrinks the diffs for cross-boundary changes by 5x, and generated code remains compatible with the existing toolchain.
We present in this article a non-parametric value-at-risk (VaR+CVaR) algorithm that remains accurate for an arbitrarily large number of underlying positions. The algorithm solves the two inherent problems of VaR estimation. First, past history is not directly applicable to the future, but all predictions of the future are based on the past. Second, VaR estimation is equivalent to modeling a single corner of a high-dimensional space (the corner where all bets lose simultaneously). The algorithm only uses mathematical methods that strictly do not degrade in accuracy at high-dimensions. Historical data are then directly incorporated with all high-dimensional relationships present, without manipulation. We test the algorithm with an ensemble of 500 portfolios with random positions across 49 distinct liquid futures of different expiries (VIX, equity indexes, gov. bonds, rates, energy, metals, livestock, agriculture, and softs). All VaR estimations are performed strictly blind to the future. The median portfolio rate of loss exceeding the 99% confidence daily VaR estimate is between $1.0\pm0.1$% depending on algorithm input parameters. 68% of portfolios have a rate of loss exceeding 99% VaR between $1.0\pm0.3$%, and 95% of portfolios between $1.0\pm0.5$%.
Siyuan Sun· Journal of Risk· 0 citations
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