Memory bandwidth in multi-socket systems based on the IBM POWER architecture (e.g., servers using the IBM POWER10 processor) scales horizontally with each socket, but the effective scaling depends heavily on NUMA locality, interconnect bandwidth, and workload behavior.

🔹 1. Linear Scaling in Ideal Conditions

Each socket in a POWER system has:

• Its own memory controllers
• Its own memory channels (DDR)

👉 So theoretically:

Total Memory Bandwidth = Sum of bandwidth from all sockets

Example:

• 1 socket → X GB/s
• 2 sockets → ~2X GB/s
• 4 sockets → ~4X GB/s

👉 This is near-linear scaling when:

• Workload is well-partitioned
• Memory access is mostly local

🔹 2. NUMA Dependency (Critical Factor)

Because POWER systems are NUMA architectures:

✅ Best Case (NUMA-aware):

• Threads access local memory only
• Each socket uses its own bandwidth

👉 Result:

• Close to linear scaling

❌ Worst Case (NUMA-unaware):

• Threads frequently access remote memory

👉 Result:

• Requests go over interconnect (PowerAXON)
• Increased latency + limited bandwidth

👉 Effective bandwidth:

• Drops significantly below theoretical maximum

🔹 3. Inter-Socket Bandwidth Limits

Sockets are connected via high-speed fabric:

• POWER9 → X-Bus / NVLink
• POWER10 → PowerAXON fabric

👉 Important:

• Interconnect bandwidth is lower than local memory bandwidth

So:

• Remote memory access becomes a bottleneck under heavy load

🔹 4. Bandwidth Contention & Saturation

Each socket:

• Has finite memory bandwidth

If many cores/threads:

• Compete for memory access

👉 Effects:

• Bandwidth saturation
• Increased memory latency

Even if total system bandwidth is high:

• Poor distribution → localized bottlenecks

🔹 5. SMT Impact on Bandwidth

With SMT (SMT4 / SMT8):

• More threads per core generate memory requests

👉 Result:

• Higher bandwidth demand per socket

Trade-off:

• SMT improves utilization
• But can increase pressure on memory subsystem

🔹 6. Cache Hierarchy as Bandwidth Multiplier

Large caches (especially L3 in POWER systems):

• Reduce need for DRAM access

👉 Effect:

• “Effective bandwidth” increases
• Because fewer requests reach memory

🔹 7. Workload Characteristics

📊 Sequential / Streaming Workloads

• Example: analytics, ETL

👉 Behavior:

• Fully utilize memory channels
• Scale well across sockets

🔀 Random Access Workloads

• Example: OLTP databases

👉 Behavior:

• Lower bandwidth utilization
• More sensitive to latency

🔁 Shared Data Workloads

• Frequent cross-socket access

👉 Behavior:

• Increased coherency traffic
• Reduced effective bandwidth

🔹 8. Real-World Scaling Behavior

🔹 9. POWER10 Enhancements

Compared to earlier generations:

• Higher per-socket bandwidth
• Improved PowerAXON interconnect
• Better prefetching and memory scheduling
• Enhanced cache hierarchy efficiency

👉 Result:

• Better scaling under real-world workloads

🔹 10. Practical Implications for Databases

📊 Oracle / DB2

Good scaling when:

• Data partitioned across sockets
• Sessions pinned to local memory

Poor scaling when:

• Shared hot data across sockets
• Frequent cross-node access

🔑 Key Insight

In multi-socket IBM POWER architecture systems, bandwidth doesn’t just scale with hardware—it scales with data locality.

Bottom Line

• Theoretical scaling: Near-linear with more sockets
• Real-world scaling: Depends on NUMA awareness

👉 Best performance comes from:

• Keeping memory access local to each socket
• Minimizing cross-socket traffic