Performance Report

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I. Introduction

• Compute Performance
  Phala’s TEE environment demonstrates exceptional computational efficiency:
  • CPU: 1,271 MIPS for per-core compression
  • Scaling: 79.91% efficiency at 32 threads (10% better than bare metal)
  • GPU: 3,341 TFLOPS for INT8 operations (6.9x AWS GPUs)
• Storage I/O
  Storage performance shows minimal overhead for most real-world scenarios:
  • Random Read: ~40% latency increase in TEE
  • Sequential I/O: Matches bare-metal at 36.2 GB/s
  • Caching: Significantly improves random read performance
• Network & Security
  The architecture maintains robust performance under demanding conditions:
  • Gateway: 1,244 QPS at 1,000 concurrent connections
  • HTTPS: 702% latency increase vs HTTP (mitigated by optimizations)
• Zero-Knowledge Proofs
  Our implementation balances security and performance:
  • Baseline: 5.12x slowdown (18.3 kHz vs non-TEE)
  • GPU Acceleration: Reduces overhead to < 1.8x
  • Throughput: 25.1k proofs/second (GPU-accelerated)

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II. Storage Performance Analysis [1]

# Run  
docker run -it --rm -v /fio:/data infrabuilder/fio

# https://github.com/InfraBuilder/docker-fio/blob/main/benchmark.sh

1. TEE CVM shows 60-80% I/O performance degradation vs bare-metal
2. Memory encryption extends I/O path and introduces context-switching overhead
3. Random operations most impacted (20% of bare-metal performance)
4. Sequential Read speed is faster in TEE VM due to that qemu may cache the memory

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III. Network Gateway Performance [2]

ab -n 5000 -c CONCURRENCY_NUMBER https://e7cc25b0992a0e16b3377652efca9c0a6559d407-8090.app.kvin.wang:12004/prpc/Version

• Experiment Setup
• gateway ver: git 7bc9eea958bd8aaca228341139f2cff5fab1d8d9

URL=https://health.app.kvin.wang:18714/
AB="ab -n 5000 -c 40 $URL"
for _ in `seq 1 50`; do
$AB &
done
$AB
sleep 1

• Results

1. TDX-Lab outperforms Prod5 across all concurrency levels
2. TLS handshake accounts for 70% of TProxy overhead
3. Info-level logging reduces Prod8 performance by 33%

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IV. zkTLS Performance in TEE [3]

1. Memory encryption causes 70-80% bandwidth degradation
2. Data structure padding increases proof size by 289%
3. Data migration overhead increases by 200% in TEE GPU

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V. Multi-threaded Computing Capability [4]

7z b -mmt8

1. TDX-Lab excels in compute-intensive tasks (high single-core frequency)
2. Prod8 leads in memory-bound operations (DDR5 advantage)
3. Prod5 suffers from frequency instability (48.7% fluctuation)

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VI. zkVM + TEE GPU Integration [5]

Cost-Performance Ratio = (Compute/Cost) / H200 Baseline

1. Seamless deployment: SP1 zkVM runs on TEE GPU without code modifications
2. Dual security: Hardware encryption + cryptographic verifiability
3. Memory advantage: Supports complex workloads (zkEVMs, 100B+ parameter models)
4. Optimization headroom: Current utilization < 30% of available resources

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VII. TEE Scalability in Large-Scale LLM Inference [6]

• Table 1: Throughput Comparison (Tokens/Requests per Second)*

1. Inverse Efficiency Scaling
   • Overhead decreases exponentially with model size
   • 70B models show near-zero overhead (H100: -0.13% TPS, -0.41% TTFT)
   • 8B models sustain 6-25% overhead due to shorter compute phases
2. Computation/IO Asymmetry

   Model	GPU Time Dominance	TEE Overhead
   Llama-3.1-70B	99.2%	< 0.5%
   Phi3-14B-128k	95.7%	2-7%
   Llama-3.1-8B	88.3%	7-26%

3. Token Volume Law
   • Every 10k token increase reduces overhead by 37%
   • At >50k tokens, TEE efficiency exceeds 95%
   • Phi3-128k demonstrates 5.29% ITL overhead vs 10.33% for 8B model

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VIII. CPU Scaling Efficiency Analysis [7]

for threads in 1 2 4 8 16 32; do echo -e "\n=== Running with $threads threads ===\n"; 7z b -mmt$threads; done

• Table 1: TEE CVM Scaling Performance (7-Zip Benchmark)*

• Table 2: Bare Metal Scaling Performance (7-Zip Benchmark)*

1. Superlinear Scaling in TEE
   • At 2 threads: 140.75% efficiency vs bare metal’s 87.3%
   • At 4 threads: 116.9% efficiency (+36% advantage over bare metal)
2. Memory Encryption Optimization

   Thread Range	Efficiency Delta	Primary Benefit
   1-8 threads	+35.2% avg	Cache locality optimization
   16-32 threads	+12.1% avg	Reduced context-switching

3. Total Performance Impact
   • Single-thread penalty: 45% performance loss in TEE (2,803 vs 4,544)
   • 32-thread recovery: 73.2% of bare metal throughput (71,665 vs 97,853)

1. TEE demonstrates superior scaling efficiency (avg +35% at ≤8 threads) due to encrypted memory access optimizations
2. Scaling beyond 16 threads becomes memory-bound, reducing TEE’s relative advantage
3. Maximum throughput reaches 73% of bare metal in fully-loaded scenarios

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References

1. IO Benchmark with FIO
2. Gateway Benchmark Analysis
3. zkTLS in TEE zkVM Benchmark
4. TDX Host Benchmark
5. SP1 zkVM in TEE H200 Performance Benchmark
6. TEE Scalability in Large-Scale LLM Inference. (2024). arXiv:2409.03992
7. dstack CPU Benchmark