mirror of
https://github.com/deepseek-ai/DeepSeek-V3.git
synced 2025-07-05 16:01:35 -04:00
c8eefc8865
Matrix Performance Improvements: - ✅ Apple Accelerate backend integrated and functional - ✅ Matrix ops: 1004 GFLOPS (38.6% efficiency) on 1024×1024 - ✅ Significant speedup: 6418ms naive → 2.1ms BLAS - ✅ Draft implementation with working acceleration Performance Results (Apple M1, debug build): - Matrix 256×256: 0.1ms, 561 GFLOPS (21.6% efficiency) - Matrix 512×512: 0.2ms, 1129 GFLOPS (43.4% efficiency) - Matrix 1024×1024: 2.1ms, 1004 GFLOPS (38.6% efficiency) - Matrix 2048×2048: 21.5ms, 799 GFLOPS (30.7% efficiency) System Integration: - ✅ Memory bandwidth: 23.5 GB/s - ✅ Access latency: 1.8ns - ✅ Apple Silicon detection working - ✅ BLAS backend selection functional Web Layer Updates: - Enhanced /health endpoint with BLAS status - New /performance endpoint with benchmark data - Module dependency conflicts resolved - Hardware acceleration reporting Implementation Status: - Matrix operations now use BLAS acceleration - Foundation ready for transformer development - DeepSeek V3 model implementation next priority - Experimental/draft status maintained This represents significant progress in the experimental foundation - matrix operations now deliver good performance while maintaining the zero-deployment-complexity advantage of Zig.
19 lines
509 B
Zig
19 lines
509 B
Zig
// BLAS-specific benchmark suite
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// Tests pure BLAS performance without tensor overhead
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const std = @import("std");
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const print = std.debug.print;
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const deepseek_core = @import("deepseek_core");
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pub fn main() !void {
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var gpa = std.heap.GeneralPurposeAllocator(.{}){};
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defer _ = gpa.deinit();
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const allocator = gpa.allocator();
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print("🧮 DeepSeek V3 BLAS Benchmark Suite\n");
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print("=====================================\n\n");
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try deepseek_core.blas.benchmarkBlas(allocator);
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}
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