parallel computing security side channel

**Security in Parallel Computing** is the **emerging discipline addressing the unique attack surfaces introduced by shared parallel hardware — where multiple tenants sharing GPU compute, CPU caches, DRAM rows, and network fabrics create side-channel leakage opportunities that allow one tenant to infer sensitive information about another's computation, requiring architectural mitigations and secure programming practices that often conflict with maximum performance**. **Shared Hardware Attack Surfaces** - **Shared LLC (Last-Level Cache)**: cache timing attacks (Prime+Probe, Flush+Reload) allow a co-located attacker to monitor cache access patterns of a victim, inferring cryptographic keys or private data. - **DRAM Row Hammer**: repeated access to DRAM rows induces bit flips in adjacent rows, enabling privilege escalation or data corruption across VM boundaries. - **GPU Shared Resources**: GPU L2 cache timing, memory bus contention, and power consumption are observable by co-tenant processes, leaking information about ML model architectures or input data. - **Network Contention**: measuring response latency reveals information about co-tenant traffic patterns. **Spectre and Meltdown in HPC** Spectre exploits speculative execution: trick the CPU into speculatively accessing out-of-bounds memory, leak data through cache timing side channel. Meltdown exploits privilege bypass in speculative execution. Patches (KPTI, retpoline) add 5-30% overhead — significant in HPC. Cloud HPC providers must patch, impacting all tenants. **Secure Multi-Party Computation (MPC)** - **Homomorphic Encryption (HE)**: compute on encrypted data (BFV, BGV, CKKS schemes), no decryption needed. 100x-10000x overhead vs plaintext. GPU acceleration (cuFHE, SEAL-GPU) reduces overhead. - **Garbled Circuits**: two-party secure computation where function is represented as boolean circuit garbled by one party. O(|circuit|) communication overhead. - **Secret Sharing** (SPDZ): secret split across parties, compute on shares without learning secret. Used in federated learning. **Confidential Computing in Cloud HPC** - **AMD SEV (Secure Encrypted Virtualization)**: VM memory encrypted with per-VM key inaccessible to hypervisor, SEV-SNP adds integrity protection. - **Intel TDX (Trust Domain Extensions)**: hardware-isolated VMs (Trust Domains) with encrypted memory, remote attestation. - **ARM CCA (Confidential Compute Architecture)**: realm VMs for cloud. - Performance overhead: 5-15% for memory encryption. **GPU Security Challenges** - GPU VRAM not cleared between processes by default (historical): risk of data leakage. Modern drivers zero GPU memory on process exit. - GPU SM context isolation: NVIDIA MIG (Multi-Instance GPU) provides hardware isolation between tenants. - GPU-side-channel research: power side channel (RAPL for CPU, NVML for GPU power) can leak model weights. Security in Parallel Computing is **the critical but underappreciated discipline ensuring that performance-driven architectural features — speculation, caching, memory sharing — do not become pathways for information leakage, requiring the HPC and security communities to collaborate on architectures that are simultaneously fast, scalable, and provably isolated**.